Dirk Holz
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Preview abstract
This book includes the post-conference proceedings of the 22nd RoboCup International Symposium, held in Montreal, QC, Canada, in June 2018.
The 32 full revised papers and 11 papers from the winning teams presented were carefully reviewed and selected from 51 submissions.
This book highlights the approaches of champion teams from the competitions and documents the proceedings of the 22nd annual RoboCup International Symposium. Due to the complex research challenges set by the RoboCup initiative, the RoboCup International Symposium offers a unique perspective for exploring scientific and engineering principles underlying advanced robotic and AI systems.
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A Standard for Map Data Representation: IEEE 1873-2015 Facilitates Interoperability Between Robots
Francesco Amigoni
Wonpil Yu
Torsten Andre
Martin Magnusson
Matteo Matteucci
Hyungpil Moon
Masashi Yokotsuka
Geoffrey Biggs
Raj Madhavan
IEEE Robotics & Automation Magazine, 25(2018), pp. 65-76
Preview abstract
The availability of environment maps for autonomous robots enables them to complete several tasks. A new IEEE standard, IEEE 1873-2015, Robot Map Data Representation for Navigation (MDR) [15], sponsored by the IEEE Robotics and Automation Society (RAS) and approved by the IEEE Standards Association Standards Board in September 2015, defines a common representation for two-dimensional (2-D) robot maps and is intended to facilitate interoperability among navigating robots. The standard defines an extensible markup language (XML) data format for exchanging maps between different systems. This article illustrates how metric maps, topological maps, and their combinations can be represented according to the standard.
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Example implementations relate to map generation and alignment. For instance, a computing system may receive and use sensor data indicative of positions of multiple markers positioned relative to a sensor within an environment to determine a pose of the sensor and also create a map that indicates the markers positions. The computing system may also receive and use subsequent sensor data indicative of distances from the sensor to surfaces in the environment and the determined pose of the sensor to determine an occupancy grid map that represents the surfaces within the environment. The computing system may then determine a transformation between the map of the markers and a design model of the environment that relates occupied cells in the occupancy grid map to sampled points from the design model, and provide the transformation between the map of the plurality of markers and the design model.
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Examples relate to simultaneous localization and calibration. An example implementation may involve receiving sensor data indicative of markers detected by a sensor on a vehicle located at vehicle poses within an environment, and determining a pose graph representing the vehicle poses and the markers. For instance, the pose graph may include edges associated with a cost function representing a distance measurement between matching marker detections at different vehicle poses. The distance measurement may incorporate the different vehicle poses and a sensor pose on the vehicle. The implementation may further involve determining a sensor pose transform representing the sensor pose on the vehicle that optimizes the cost function associated with the edges in the pose graph, and providing the sensor pose transform. In further examples, motion model parameters of the vehicle may be optimized as part of a graph-based system as well or instead of sensor calibration.
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Preview abstract
Methods and systems for detecting sensor orientation characteristics using marker-based localization are disclosed herein. In one aspect, a robotic device can: receive a map of a horizontal marker plane that includes mapped positions of a first marker and a second marker arranged in the horizontal marker plane; receive, from a sensor configured to scan a two-dimensional sensor plane, sensor data indicative of positions of the first and second markers relative to the sensor; determine measured positions of the first and second markers based on the sensor data and a current position of the sensor; determine a difference vector between a first vector that connects the mapped positions of the first and second markers and a second vector that connects the measured positions of the first and second markers; and determine, based on the difference vector, an orientation of the two-dimensional sensor plane relative to the horizontal marker plane.
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Localization with Negative Mapping
Patent(2018)
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Example embodiments include determining a map of an environment of a robotic vehicle. The map includes locations of a plurality of mapped landmarks within the environment and a false detection source region within the environment. The embodiments further include detecting a plurality of candidate landmarks, and determining which of the detected candidate landmarks correspond to one of the plurality of mapped landmarks and which correspond to false detections. The embodiments additionally include estimating a pose of the robotic vehicle within the environment. The embodiments further include determining which of the detected candidate landmarks determined to correspond to false detections fall within the false detection source region. The embodiments still further include determining a confidence level of the pose estimate based on which of the detected candidate landmarks determined to correspond to false detections fall within the false detection source region.
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Example implementations may relate methods and systems for detecting, recognizing, and localizing pallets. For instance, a computing system may receive sensor data representing aspects of an environment, and identify a set of edge points in the sensor data. The computing system may further determine a set of line segments from the set of edge points where each line segment may fit to a subset of the set of edge points. Additionally, the computing system may also filter the set of line segments to exclude line segments that have a length outside a height range and a width range associated with dimensions of a pallet template, and identify, from the filtered set of line segments, a subset of line segments that align with the pallet template. Based on the identified subset of line segments, the computing system may determine a pose of a pallet in the environment.
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Localization of Robotic Vehicles
Patent(2018)
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An example method includes determining locations of a plurality of candidate landmarks in relation to a robot based on sensor data from at least one sensor on the robot. The method further includes determining a plurality of sample sets, wherein each sample set comprises a subset of the plurality of candidate landmarks and a plurality of corresponding mapped landmarks. The method also includes determining a transformation for each sample set that relates the candidate landmarks from the subset to the corresponding mapped landmarks. The method additionally includes applying the determined transformation for each sample set to the plurality of candidate landmarks to determine a number of inliers associated with each sample set based on distances between the transformed plurality of candidate landmarks and a plurality of neighbouring mapped landmarks. The method further includes selecting a sample set from the plurality based on the number of inliers associated with each sample set. The method still further includes estimating a pose of the robot based on the selected sample set.
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Landmark Placement for Localization
Patent(2018)
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Embodiments are provided that include receiving sensor data from a sensor positioned at a plurality of positions in an environment. The environment includes a plurality of landmarks. The embodiments also include determining, based on the sensor data, a subset of the plurality of landmarks detected at each of the plurality of positions. The embodiments further include determining, based on the subset of the plurality of landmarks detected at each of the plurality of positions, a detection frequency of each landmark. The embodiments additionally include determining, based on the determined detection frequency of each landmark, a localization viability metric associated with each landmark. The embodiments still further include providing for display, via a user interface, a map of the environment. The map includes an indication of the localization viability metric associated with each landmark.
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SkiROS — A skill-based robot control platform on top of ROS
Francesco Rovida
Matthew Crosby
Athanasios S. Polydoros
Bjarne Großmann
Ronald P. A. Petrick
Volker Krüger
Robot Operating System (ROS), Springer International Publishing(2017), pp. 121-160
Preview abstract
The development of cognitive robots in ROS still lacks the support of some key components: a knowledge integration framework and a framework for autonomous mission execution. In this research chapter, we will discuss our skill-based platform SkiROS, that was developed on top of ROS in order to organize robot knowledge and its behavior. We will show how SkiROS offers the possibility to integrate different functionalities in form of skill ‘apps’ and how SkiROS offers services for integrating these skill-apps into a consistent workspace. Furthermore, we will show how these skill-apps can be automatically executed based on autonomous, goal-directed task planning. SkiROS helps the developers to program and port their high-level code over a heterogeneous range of robots, meanwhile the minimal Graphical User Interface (GUI) allows non-expert users to start and supervise the execution. As an application example, we present how SkiROS was used to vertically integrate a robot into the manufacturing system of PSA Peugeot-Citroën. We will discuss the characteristics of the SkiROS architecture which makes it not limited to the automotive industry but flexible enough to be used in other application areas as well. SkiROS has been developed on Ubuntu 14.04 LTS and ROS indigo and it can be downloaded at https://github.com/frovida/skiros. A demonstration video is also available at https://youtu.be/mo7UbwXW5W0.
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Multi-Layered Mapping and Navigation for Autonomous Micro Aerial Vehicles
David Droeschel
Matthias Nieuwenhuisen
Marius Beul
Jörg Stückler
Sven Behnke
Journal of Field Robotics, 33(4)(2016), pp. 451-475
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Micro aerial vehicles, such as multirotors, are particularly well suited for the autonomous monitoring, inspection, and surveillance of buildings, e.g., for maintenance or disaster management. Key prerequisites for the fully autonomous operation of micro aerial vehicles are real-time obstacle detection and planning of collision-free trajectories. In this article, we propose a complete system with a multimodal sensor setup for omnidirectional obstacle perception consisting of a 3D laser scanner, two stereo camera pairs, and ultrasonic distance sensors. Detected obstacles are aggregated in egocentric local multiresolution grid maps. Local maps are efficiently merged in order to simultaneously build global maps of the environment and localize in these. For autonomous navigation, we generate trajectories in a multi-layered approach: from mission planning over global and local trajectory planning to reactive obstacle avoidance. We evaluate our approach and the involved components in simulation and with the real autonomous micro aerial vehicle. Finally, we present the results of a complete mission for autonomously mapping a building and its surroundings.
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Approximate Surface Reconstruction and Registration for RGB-D SLAM
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RGB-D cameras have attracted much attention in the fields of robotics and computer vision, especially for object modeling and environment mapping. A key problem in all these applications is the registration of sequences of RGB-D images. In this paper, we present an efficient yet reliable approach to align pairs and sequences of RGB-D images that makes use of local surface information. We extend previous works on 3D mapping with micro aerial vehicles to sequences of RGB-D images. The resulting alignment is based on a robust surface-to-surface error metric and uses multiple surface-to-surface patch matches between pairs of RGB-D images. Quantitative evaluations show that our approach is competitive with state-of-the-art approaches.
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RoboCup@Home — Benchmarking Domestic Service Robots
Sven Wachsmuth
Maja Rudinac
Jaview Ruiz-del-Solar
AAAI Conference on Artificial Intelligence (AAAI), Austin, Texas, USA(2015), pp. 4328-4329
Preview abstract
The RoboCup@Home league has been founded in 2006 with the idea to drive research in AI and related fields towards autonomous and interactive robots that cope with real life tasks in supporting humans in everday life. The yearly competition format establishes benchmarking as a continuous process with yearly changes instead of a single challenge. We discuss the current state and future perspectives of this endevour.
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Registration with the Point Cloud Library: A Modular Framework for Aligning in 3-D
Alexandru E. Ichim
Radu B. Rusu
Sven Behnke
IEEE Robotics & Automation Magazine, 22, no 4(2015), pp. 110-124
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Registration is an important step when processing three-dimensional (3-D) point clouds. Applications for registration range from object modeling and tracking, to simultaneous localization and mapping (SLAM). This article presents the open-source point cloud library (PCL) and the tools available for point cloud registration. The PCL incorporates methods for the initial alignment of point clouds using a variety of local shape feature descriptors, as well as methods for refining initial alignments using different variants of the well-known iterative closest point (ICP) algorithm. This article provides an overview on registration algorithms, usage examples of their PCL implementations, and tips for their application. Since the choice and parameterization of the right algorithm for a particular type of data is one of the biggest problems in 3-D point cloud registration, we present three complete examples of data (and applications) and the respective registration pipeline in the PCL. These examples include dense red-green-blue-depth (RGB-D) point clouds acquired by consumer color and depth cameras, high-resolution laser scans from commercial 3-D scanners, and low-resolution sparse point clouds captured by a custom lightweight 3-D scanner on a microaerial vehicle (MAV).
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Evaluation of Registration Methods for Sparse 3D Laser Scans
Jan Razlaw
David Droeschel
Sven Behnke
European Conference on Mobile Robots (ECMR), Lincoln, UK(2015)
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The registration of 3D laser scans is an important task in mapping applications. For the task of mapping with autonomous micro aerial vehicles (MAVs), we have developed a light-weight 3D laser scanner. Since the laser scanner is rotated quickly for fast omnidirectional obstacle perception, the acquired point clouds are particularly sparse and registration becomes challenging. In this paper, we present a thorough experimental evaluation of registration algorithms in order to determine the applicability of both the scanner and the registration algorithms. Using the estimated poses of the MAV, we aim at building local egocentric maps for both collision avoidance and 3D mapping. We use multiple metrics for assessing the quality of the different pose estimates and the quality of the resulting maps. In addition, we determine for all algorithms optimal sets of parameters for the challenging data. We make the recorded datasets publicly available and present results showing both the best suitable registration algorithm and the best parameter sets as well as the quality of the estimated poses and maps.
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Registration of non-uniform density 3D laser scans for mapping with micro aerial vehicles
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Micro aerial vehicles (MAVs) pose specific constraints on onboard sensing, mainly limited payload and limited processing power. For accurate 3D mapping even in GPS-denied environments, we have designed a lightweight 3D laser scanner specifically for the application on MAVs. Similar to other custom-built 3D laser scanners composed of a rotating 2D laser range finder, it exhibits different point densities within and between individual scan lines. When rotated fast, such non-uniform point densities influence neighborhood searches which in turn may negatively affect local feature estimation and scan registration. We present a complete pipeline for 3D mapping including pair-wise registration and global alignment of such non-uniform density 3D point clouds acquired in-flight. For registration, we extend a state-of-the-art registration algorithm to include topological information from approximate surface reconstructions. For global alignment, we use a graph-based approach making use of the same error metric and iteratively refine the complete vehicle trajectory. In experiments, we show that our approach can compensate for the effects caused by different point densities up to very low angular resolutions and that we can build accurate and consistent 3D maps in-flight with a micro aerial vehicle.
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Real-Time Object Detection, Localization and Verification for Fast Robotic Depalletizing
Angeliki Topalidou-Kyniazopoulou
Joerg Stueckler
Sven Behnke
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany(2015)
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Depalletizing is a challenging task for manipulation robots. Key to successful application are not only robustness of the approach, but also achievable cycle times in order to keep up with the rest of the process. In this paper, we propose a system for depalletizing and a complete pipeline for detecting and localizing objects as well as verifying that the found object does not deviate from the known object model, e.g., if it is not the object to pick. In order to achieve high robustness (e.g., with respect to different lighting conditions) and generality with respect to the objects to pick, our approach is based on multi-resolution surfel models. All components (both software and hardware) allow operation at high frame rates and, thus, allow for low cycle times.
In experiments, we demonstrate depalletizing of automotive and other prefabricated parts with both high reliability (w.r.t. success rates) and efficiency (w.r.t. low cycle times).
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A Skill-Based System for Object Perception and Manipulation for Automating Kitting Tasks
Angeliki Topalidou-Kyniazopoulou
Francesco Rovida
Mikkel Rath Pedersen
Volker Krüger
Sven Behnke:
IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Luxembourg(2015)
Preview abstract
The automation of kitting tasks—collecting a set of parts for one particular car into a kit—has a huge impact in the automotive industry. It considerably increases the automation levels of tasks typically conducted by human workers. Collecting the parts involves picking up objects from pallets and bins as well as placing them in the respective compartments of the kitting box. In this paper, we present a complete system for automated kitting with a mobile manipulator thereby focusing on depalletizing tasks and placing. In order to allow for low cycle times, we present particularly efficient solutions to object perception as well as motion planning and execution. For easy portability to different platforms,
all components are integrated into a skill-based framework that is tightly coupled with a task planning component. We present results of experiments at both a research laboratory environment and at the industrial site of PSA Peugeot Citroen serving as a proof of concept for the overall system design and implementation.
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RoboCup@Home: Analysis and results of evolving competitions for domestic and service robots
Luca Iocchi
Javier Ruiz-del-Solar
Komei Sugiura
Tijnvan der Zant
Artificial Intelligence, 229(2015), pp. 258-281
Preview abstract
Scientific competitions are becoming more common in many research areas of artificial intelligence and robotics, since they provide a shared testbed for comparing different solutions and enable the exchange of research results. Moreover, they are interesting for general audiences and industries. Currently, many major research areas in artificial intelligence and robotics are organizing multiple-year competitions that are typically associated with scientific conferences.
One important aspect of such competitions is that they are organized for many years. This introduces a temporal evolution that is interesting to analyze. However, the problem of evaluating a competition over many years remains unaddressed. We believe that this issue is critical to properly fuel changes over the years and measure the results of these decisions. Therefore, this article focuses on the analysis and the results of evolving competitions.
In this article, we present the RoboCup@Home competition, which is the largest worldwide competition for domestic service robots, and evaluate its progress over the past seven years. We show how the definition of a proper scoring system allows for desired functionalities to be related to tasks and how the resulting analysis fuels subsequent changes to achieve general and robust solutions implemented by the teams. Our results show not only the steadily increasing complexity of the tasks that RoboCup@Home robots can solve but also the increased performance for all of the functionalities addressed in the competition.
We believe that the methodology used in RoboCup@Home for evaluating competition advances and for stimulating changes can be applied and extended to other robotic competitions as well as to multi-year research projects involving Artificial Intelligence and Robotics.
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On RoboCup@Home — Past, Present and Future of a Scientific Competition for Service Robots.
Jaview Ruiz-del-Solar
Komei Sugiura
Sven Wachsmuth
Robot World Cup XVIII, Lecture Notes in Computer Science(2014), pp. 686-697
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RoboCup@Home is an application-oriented league within the annual RoboCup events. It focuses on domestic service robots and mobile manipulators interacting with human users. Participating robots need to solve tasks ranging from following and guiding human users to delivering objects, e.g., in a supermarket.
In this paper, we present the @Home league and how it evolved over the last seven years since its existence. We place particular emphasis on how we evaluate the teams’ performances over the years and how we use the obtained statistics to drive the development of the league. This process is shown in detail on two examples—following human guides, and finding and manipulating objects. Finally, we will outline possible future directions and developments.
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Mapping with Micro Aerial Vehicles by Registration of Sparse 3D Laser Scans
Sven Behnke
International Conference on Intelligent Autonomous Systems (IAS), Padova, Italy(2014)
Preview abstract
Micro aerial vehicles (MAVs) pose specific constraints on onboard sensing, mainly limited payload and limited processing power. For accurate 3D mapping even in GPS denied environments, we have designed a light-weight 3D laser scanner specifically for the application on MAVs. Similar to other custom-built 3D laser scanners composed of a rotating 2D laser range finder, it exhibits different point densities within and between individual scan lines. When rotated fast, such non-uniform point densities influence neighborhood searches which in turn may negatively affect local feature estimation and scan registration. We present a complete pipeline for 3D mapping including pair-wise registration and global alignment of 3D scans acquired in-flight. For registration, we extend a state-of-the-art registration algorithm to include topological information from approximate surface reconstructions. For global alignment, we use a graph-based approach making use of the same error metric and iteratively refine the complete vehicle trajectory. In experiments, we show that our approach can compensate for the effects caused by different point densities up to very low angular resolutions and that we can build accurate and consistent 3D maps in-flight with a micro aerial vehicle.
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Active Recognition and Manipulation for Mobile Robot Bin Picking
Matthias Nieuwenhuisen
David Droeschel
Jörg Stückler
Alexander Berner
Jun Li
Reinhard Klein
Sven Behnke
Gearing up and accelerating cross-fertilization between academic and industrial robotics research in Europe - Technology transfer experiments from the ECHORD project, vol 94 of Springer Tracts in Advanced Robotics (STAR)(2014), pp. 133-153
Preview abstract
Grasping individual objects from an unordered pile in a box has been investigated in stationary scenarios so far. In this work, we present a complete system including active object perception and grasp planning for bin picking with a mobile robot. At the core of our approach is an efficient representation of objects as compounds of simple shape and contour primitives. This representation is used for both robust object perception and efficient grasp planning. For being able to manipulate previously unknown objects, we learn object models from single scans in an offline phase. During operation, objects are detected in the scene using a particularly robust probabilistic graph matching. To cope with severe occlusions we employ active perception considering not only previously unseen volume but also outcomes of primitive and object detection. The combination of shape and contour primitives makes our object perception approach particularly robust even in the presence of noise, occlusions, and missing information. For grasp planning, we efficiently pre-compute possible grasps directly on the learned object models. During operation, grasps and arm motions are planned in an efficient local multiresolution height map. All components are integrated and evaluated in a bin picking and part delivery task.
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Omnidirectional Perception for Lightweight MAVs using a Continously Rotating 3D Laser Scanner
David Droeschel
Sven Behnke
Photogrammetrie Fernerkundung Geoinformation (PFG), 5(2014), pp. 451-464
Preview abstract
Micro aerial vehicles (MAV) are restricted in their size and weight, making the design of sensory systems for these vehicles challenging. We designed a small and lightweight continuously rotating 3D laser scanner—allowing for environment perception in a range of 30 m in almost all directions. This sensor is well suited for applications such as 3D obstacle detection, 6D motion estimation, localisation, and mapping.
Reliably perceiving obstacles in the surroundings of the MAV is a prerequisite for fully autonomous flight in complex environments. Due to varying shape and reflectance properties of objects, not all obstacles are perceived in every 3D laser scan (one half rotation of the scanner). Especially farther away from the MAV, multiple scans may be necessary in order to adequately detect an obstacle. In order to increase the probability of detecting obstacles, we aggregate acquired scans over short periods of time in an egocentric grid-based map. We register acquired scans against this local map to estimate the motion of our MAV and to consistently update the map.
In experiments, we show that our approaches to pose estimation and laser scan matching allow for reliable aggregation of 3D scans over short periods of time, sufficiently accurate to improve detection probability without causing inaccuracies in the estimation of the position of detected obstacles. Furthermore, we assess the probability of detecting different types of obstacles in varying distances from the MAV.
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Registration of Non-Uniform Density 3D Point Clouds using Approximate Surface Reconstruction
Preview abstract
3D laser scanners composed of a rotating 2D laser range scanner exhibit different point densities within and between
individual scan lines. Such non-uniform point densities influence neighbor searches which in turn may negatively affect
feature estimation and scan registration. To reliably register such scans, we extend a state-of-the-art registration algorithm
to include topological information from approximate surface reconstructions. We show that our approach outperforms
related approaches in both refining a good initial pose estimate and registering badly aligned point clouds if no such estimate is available. In an example application, we demonstrate local 3D mapping with a micro aerial vehicle by registering
sequences of non-uniform density point clouds acquired in-flight with a continuously rotating lightweight 3D scanner.
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Increasing Flexibility of Mobile Manipulation and Intuitive Human-Robot Interaction in RoboCup@Home
Jörg Stückler
David Droeschel
Kathrin Gräve
Michael Schreiber
Angeliki Topalidou-Kyniazopoulou
Max Schwarz
Sven Behnke:
RoboCup 2013: Robot World Cup XVII, Lecture Notes in Computer Science 8371, Springer(2014), pp. 135-146
Preview abstract
In this paper, we describe system and approaches of our team
NimbRo@Home that won the RoboCup@Home competition 2013. We
designed a multi-purpose gripper for grasping typical household objects
in pick-and-place tasks and also for using tools. The tools are complementarily equipped with special handles that establish form closure with
the gripper, which resists wrenches in any direction. We demonstrate
tool use for opening a bottle and grasping sausages with a pair of tongs
in a barbecue scenario. We also devised efficient deformable registration
methods for the transfer of manipulation skills between objects of the
same kind but with differing shape. Finally, we enhance human-robot
interaction with a remote user interface for handheld PCs that enables
a user to control capabilities of the robot. These capabilities have been
demonstrated in the open challenges of the competition. We also explain
our approaches to the predefined tests of the competition, and report on
the performance of our robots at RoboCup 2013.
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Towards Autonomous Navigation of an UAV-based Mobile Mapping System
Lasse Klingbeil
Matthias Nieuwenhuisen
Johannes Schneider
Christian Eling
David Droeschel
Thomas Läbe
Wolfgang Förstner
Sven Behnke
Heiner Kuhlmann:
International Conference on Machine Control & Guidance (MCG)(2014)
Preview abstract
For situations, where mapping is neither possible from high altitudes nor from the ground, we are developing an autonomous micro aerial vehicle able to fly at low altitudes in close vicinity of obstacles. This vehicle is based on a MikroKopterTM octocopter platform (maximum total weight:
5kg), and contains a dual frequency GPS board, an IMU, a compass, two stereo camera pairs with
fisheye lenses, a rotating 3D laser scanner, 8 ultrasound sensors, a real-time processing unit, and a
compact PC for on-board ego-motion estimation and obstacle detection for autonomous navigation. A
high-resolution camera is used for the actual mapping task, where the environment is reconstructed in
three dimensions from images, using a highly accurate bundle adjustment. In this contribution, we
describe the sensor system setup and present results from the evaluation of several aspects of the
different subsystems as well as initial results from flight tests
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Approximate Triangulation and Region Growing for Efficient Segmentation and Smoothing of Range Images
Preview abstract
Decomposing sensory measurements into coherent parts is a fundamental prerequisite for scene understanding that is required for solving complex tasks, e.g., in the field of mobile manipulation. In this article, we describe methods for efficient segmentation of range images and organized point clouds. In order to achieve real-time performance in complex environments, we focus our approach on simple but robust solutions. We present a fast approach to surface reconstruction in range images and organized point clouds by means of approximate polygonal meshing. The obtained local surface information and neighborhoods are then used to 1) smooth the underlying measurements, and 2) segment the image into planar regions and other geometric primitives. A comparative evaluation using publicly available data sets shows that our approach achieves state-of-the-art performance while being significantly faster than other methods.
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Combining Contour and Shape Primitives for Object Detection and Pose Estimation of Prefabricated Parts
Alexander Berner
Jun Li
Jörg Stückler
Sven Behnke
Reinhard Klein
IEEE International Conference on Image Processing (ICIP), Melbourne, Australia(2013)
Preview abstract
Man-made objects such as mechanical construction parts can typically be described as a composition of shape primitives like cylinders, planes, cones and spheres. We propose a robust method for the detection and pose estimation of such objects in 3D point clouds. Our main contribution is to enhance a probabilistic graph-matching approach that detects objects using 3D shape primitives with distinct 2D primitives such as circular contours. With this extension, our method copes with difficult occlusion situations and can be applied for object manipulation in complex scenarios such as grasping from a pile or bin-picking. We demonstrate the performance of our approach in a comparison with a state-of-the-art feature-based method for objects of generic shape and a primitive-based approach using only 3D shapes and no contours.
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Benchmarking Intelligent Service Robots through Scientific Competitions: the RoboCup@Home approach
Luca Iocchi
Tijn van der Zant
AAAI Spring Symposium Designing Intelligent Robots: Reintegrating AI II(2013)
Preview abstract
The dynamical and uncertain environments of domestic service robots, which include humans, require rethinking of the benchmarking principles for testing these robots. Since 2006 RoboCup@Home has used statistical procedures to track and steer the progress of domestic service robots. This paper explains the procedures and shows outcomes of these international benchmarking efforts. Although aspects such as shopping in a supermarket receive a fair amount of attention in the robotics community, the authors believe that a recently implemented test is the most important outcome of RoboCup@Home, namely the benchmarking of robot cognition.
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Towards Multimodal Omnidirectional Obstacle Detection for Autonomous Unmanned Aerial Vehicles
Matthias Nieuwenhuisen
David Droeschel
Michael Schreiber
Sven Behnke
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-1/W2(2013), pp. 201-206
Preview abstract
Limiting factors for increasing autonomy and complexity of truly autonomous systems (without external sensing and control) are onboard sensing and onboard processing power. In this paper, we propose a hardware setup and processing pipeline that allows a fully autonomous UAV to perceive obstacles in (almost) all directions in its surroundings. Different sensor modalities are applied in order take into account the different characteristics of obstacles that can commonly be found in typical UAV applications. We provide a complete overview on the implemented system and present experimental results as a proof of concept.
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Multimodal Obstacle Detection and Collision Avoidance for Micro Aerial Vehicles
Matthias Nieuwenhuisen
David Droeschel
Johannes Schneider
Thomas Läbe
Sven Behnke
European Conference on Mobile Robots (ECMR), Barcelona, Spain(2013)
Preview abstract
Reliably perceiving obstacles and avoiding collisions is key for the fully autonomous application of micro aerial vehicles (MAVs). Limiting factors for increasing autonomy and complexity of MAVs are limited onboard sensing and limited onboard processing power. In this paper, we propose a complete system with a multimodal sensor setup for omnidirectional obstacle perception. We developed a lightweight 3D laser scanner and visual obstacle detection using wide-angle stereo cameras. Detected obstacles are aggregated in egocentric grid maps. We implemented a fast reactive collision avoidance approach for safe operation in the vicinity of structures like buildings or vegetation.
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Evaluating the Impact of Perception and Decision Timing on Autonomous Robotic Exploration.
Francesco Amigoni
Alberto Quattrini Li
European Conference on Mobile Robots (ECMR), Barcelona, Spain(2013), pp. 68-73
Preview abstract
Autonomous robotic exploration of initially unknown environments is at the basis of several applications, including map building and search and rescue. Despite the many recent works on robotic exploration, an issue that has not been adequately addressed in the literature so far is the evaluation of the impact of the perception (for map update) and decision (about where to go next) timing on the behavior of an exploring robotic system. In this paper, we contribute to fill this gap by providing a quantitative experimental analysis of how frequencies of perception and decision influence the performance of an exploring mobile robot. Results, obtained with an experimental simulation framework (implemented and made publicly available) based on ROS and Stage, confirm the intuitive idea that the best performance is obtained with fastpaced perceptions and decisions, but also suggest some tradeoffs for the values of perception and decision frequencies in some settings.
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Mobile Bin Picking with an Anthropomorphic Service Robot
Matthias Nieuwenhuisen
David Droeschel
Jörg Stückler
Alexander Berner
Jun Li
Reinhard Klein
Sven Behnke
IEEE International Conference on Robotics and Automation (ICRA), Karlsruhe, Germany(2013)
Preview abstract
Grasping individual objects from an unordered pile in a box has been investigated in static scenarios so far. In this paper, we demonstrate bin picking with an anthropomorphic mobile robot. To this end, we extend global navigation techniques by precise local alignment with a transport box. Objects are detected in range images using a shape primitivebased approach. Our approach learns object models from single scans and employs active perception to cope with severe occlusions. Grasps and arm motions are planned in an efficient local multiresolution height map. All components are integrated and evaluated in a bin picking and part delivery task.
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Efficient 3D object perception and grasp planning for mobile manipulation in domestic environments
Jörg Stückler
Ricarda Steffens
Sven Behnke
Robotics and Autonomous Systems, 61(10)(2013), pp. 1106-1115
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In this article, we describe efficient methods for tackling everyday mobile manipulation tasks that require object pick-up. In order to achieve real-time performance in complex environments, we focus our approach on fast yet robust solutions. For 3D perception of objects on planar surfaces, we develop scene segmentation methods that process depth images in real-time at high frame rates. We efficiently plan feasible, collision-free grasps for the segmented objects directly from the perceived point clouds to achieve fast execution times. We evaluate our approaches quantitatively in lab experiments and also report on the successful integration of our methods in public demonstrations at RoboCup@Home competitions in 2011 and 2012.
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Fast Range Image Segmentation and Smoothing using Approximate Surface Reconstruction and Region Growing
Sven Behnke
International Conference on Intelligent Autonomous Systems (IAS), Jeju Island, Korea(2012)
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Decomposing sensory measurements into relevant parts is a fundamental prerequisite for solving complex tasks, e.g., in the field of mobile manipulation in domestic environments. In this paper, we present a fast approach to surface reconstruction in range images by means of approximate polygonal meshing. The obtained local surface information and neighborhoods are then used to 1) smooth the underlying measurements, and 2) segment the image into planar regions and other geometric primitives. An evaluation using publicly available data sets shows that our approach does not rank behind state-of-the-art algorithms while allowing to process range images at high frame rates.
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Johnny: An Autonomous Service Robot for Domestic Environments
Thomas Breuer
Geovanny Giorgana Macedo
Ronny Hartanto
Nico Hochgeschwender
Frederik Hegger
Zha Jin
Christian Müller
Jan Paulus
Mike Reckhaus
José Álvarez Ruiz
Paul Plöger
Gerhard K. Kraetzschmar
Journal of Intelligent & Robotic Systems, 66(1-2)(2012), pp. 245-272
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In this article we describe the architecture, algorithms and real-world benchmarks performed by Johnny Jackanapes, an autonomous service robot for domestic environments. Johnny serves as a research and development platform to explore, develop and integrate capabilities required for real-world domestic service applications. We present a control architecture which allows to cope with various and changing domestic service robot tasks. A software architecture supporting the rapid integration of functionality into a complete system is as well presented. Further, we describe novel and robust algorithms centered around multi-modal human robot interaction, semantic scene understanding and SLAM. Evaluation of the complete system has been performed during the last years in the RoboCup@Home competition where Johnnys outstanding performance led to successful participation. The results and lessons learned of these benchmarks are explained in more detail.
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Towards Robust Mobility, Flexible Object Manipulation, and Intuitive Multimodal Interaction for Domestic Service Robots.
Jörg Stückler
David Droeschel
Kathrin Gräve
Jochen Kläß
Michael Schreiber
Ricarda Steffens
Sven Behnke.
Robot Soccer World Cup XV, LNCS 7416(2012), pp. 51-62
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In this paper, we detail the contributions of our team NimbRo to the RoboCup @Home league in 2011. We explain design and rationale of our domestic service robot Cosero that we used for the first time in a competition in 2011. We demonstrated novel capabilities in the league such as real-time table-top segmentation, flexible grasp planning, and real-time tracking of objects. We also describe our approaches to humanrobot cooperative manipulation and 3D navigation. Finally, we report on the use of our approaches and the performance of our robots at RoboCup 2011.
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Demonstrating Everyday Manipulation Skills in RoboCup@Home
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The RoboCup@Home league is a benchmark for domestic service robot systems. It evaluates approaches to mobile manipulation and human-robot interaction by testing integrated systems. In this article, we detail the contributions of our team NimbRo, with which we won the RoboCup@Home competition in 2011. We demonstrated novel capabilities in the league such as real-time tabletop segmentation, flexible grasp planning, and real-time tracking of objects. We also describe our approach to human-robot cooperative manipulation using compliant control. We report on the use of our approaches and the performance of
our robots at RoboCup 2011.
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A comparative evaluation of exploration strategies and heuristics to improve them
Nicola Basilico
Francesco Amigoni
Sven Behnke
European Conference on Mobile Robots (ECMR), Örebro, Sweden(2011), pp. 25-30
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Exploration strategies play an important role in influencing the performance of an autonomous mobile robot exploring and mapping an unknown environment. Although several exploration strategies have been proposed in the last years, their experimental evaluation and comparison are still largely unaddressed. In this paper, we quantitatively evaluate exploration strategies by experimentally comparing, in a simulation setting, a representative sample of techniques taken from literature. From a broader perspective, our work also contributes to the development of good experimental methodologies in the field of autonomous mobile robotics by promoting the principles of comparison, reproducibility, and repeatability of experiments.
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Towards Joint Attention for a Domestic Service Robot – Person Awareness and Gesture Recognition using Time-of-Flight Cameras
David Droeschel
Jörg Stückler
Sven Behnke
IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China(2011)
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Joint attention between a human user and a robot is essential for effective human-robot interaction. In this work, we propose an approach to person awareness and to the perception of showing and pointing gestures for a domestic service robot. In contrast to previous work, we do not require the person to be at a predefined position, but instead actively approach and orient towards the communication partner. For perceiving showing and pointing gestures and for estimating the pointing direction a Time-of-Flight camera is used. Estimated pointing directions and shown objects are matched to objects in the robot’s environment. Both the perception of showing and pointing gestures as well as the accurary of estimated pointing directions have been evaluated in a set of different experiments. The results show that both gestures are adequatly perceived by the robot. Furthermore, our system achieves a higher accuracy in estimating the pointing direction than is reported in the literature for a stereo-based system. In addition, the overall system has been successfully tested in two international RoboCup@Home competitions and the 2010 ICRA Mobile Manipulation Challenge.
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Hierachies of Octrees for Efficient 3D Mapping
Kai Wurm
Daniel Hennes
Radu Bogdan Rusu
Cyrill Stachniss
Kurt Konolige
Wolfram Burgard:
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), San Francisco, USA(2011)
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In this paper, we present a novel multi-resolution approach to efficiently mapping 3D environments. Our representation models the environment as a hierarchy of probabilistic 3D maps, in which each submap is updated and transformed individually. In addition to the formal description of the approach, we present an implementation for tabletop manipulation tasks and an information-driven exploration algorithm for autonomously building a hierarchical map from sensor data. We evaluate our approach using real-world as well as simulated data. The results demonstrate that our method is able to efficiently represent 3D environments at high levels of detail. Compared to a monolithic approach, our maps can be generated significantly faster while requiring significantly less memory.
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Real-Time Plane Segmentation using RGB-D Cameras
Stefan Holzer
Radu Bogdan Rusu
Sven Behnke:
Robot Soccer World Cup XV, LNCS 7416(2011), pp. 306-217
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Real-time 3D perception of the surrounding environment is a crucial precondition for the reliable and safe application of mobile service robots in domestic environments. Using a RGB-D camera, we present a system for acquiring and processing 3D (semantic) information at frame rates of up to 30Hz that allows a mobile robot to reliably detect obstacles and segment graspable objects and supporting surfaces as well as the overall scene geometry. Using integral images, we compute local surface normals. The points are then clustered, segmented, and classified in both normal space and spherical coordinates. The system is tested in different setups in a real household environment. The results show that the system is capable of reliably detecting obstacles at high frame rates, even in case of obstacles that move fast or do not considerably stick out of the ground. The segmentation of all planes in the 3D data even allows for correcting characteristic measurement errors and for reconstructing the original scene geometry in far ranges.
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Real-Time 3D Perception and Efficient Grasp Planning for Everyday Manipulation Tasks
Jörg Stückler
Ricarda Steffens
Sven Behnke
European Conference on Mobile Robots (ECMR), Örebro, Sweden(2011)
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In this paper, we describe efficient methods for solving everyday mobile manipulation tasks that require object pick-up. In order to achieve real-time performance in complex environments, we focus our approach on fast yet robust solutions. For 3D perception of objects on planar surfaces, we develop scene segmentation methods that process Microsoft Kinect depth images in real-time at high frame rates. We efficiently plan feasible, collision-free grasps on the segmented objects directly from the rerceived point clouds to achieve fast execution times. We evaluate our approaches quantitatively in lab experiments and also report on the successful integration of our methods in public demonstrations at RoboCup German Open 2011 and RoboCup 2011 in Istanbul, Turkey.
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Naive but Efficient – Using Greedy Strategies for Exploration, Inspection and Search.
Fachwissenschaftlicher Informatik-Kongress, Lecture Notes in Informatics (LNI)(2010)
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For operating in initially unknown and dynamic environments, autonomous mobile robots need abilities to explore their workspace and construct an environment model as well as to perform searches in that model and re-explore the environment to keep the model up-to-date. This paper focuses on the efficiency of using simple frontier-based greedy strategies for exploration and search that provide an autonomous mobile robot with these abilities.
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Towards Semantic Scene Analysis with Time-of-Flight Cameras
Ruwen Schnabel
David Droeschel
Jörg Stückler
Sven Behnke
RoboCup 2010: Robot Soccer World Cup XIV, Springer Lecture Notes in Computer Science, pp. 121-132
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For planning grasps and other object manipulation actions in complex environments, 3D semantic information becomes crucial. This paper focuses on the application of recent 3D Time-of-Flight (ToF) cameras in the context of semantic scene analysis. For being able to acquire semantic information from ToF camera data, we a) pre-process the data including outlier removal, filtering and phase unwrapping for correcting erroneous distance measurements, and b) apply a randomized algorithm for detecting shapes such as planes, spheres, and cylinders. We present experimental results that show that the robustness against noise and outliers of the underlying RANSAC paradigm allows for segmenting and classifying objects in 3D ToF camera data captured in natural mobile manipulation setups.
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Effiziente Kartographie und Navigation für mobile Service Roboter
GI Informatik-Spektrum, 33(4)(2010), pp. 393-397
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Das Ziel dieser Arbeit ist das Design und die Implementierung eines vollständigen Systems zur robusten Navigation mobiler Roboter in häuslichen Umgebungen. Adressierte Probleme sind die Modellierung von Umgebungen, die Planung von Pfaden sowie die Steuerung eines mobilen Roboters. Das resultierende System wurde erfolgreich in der Robocup@Home-Liga eingesetzt.
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Evaluating the efficiency of frontier-based exploration strategies
Nicola Basilico
Francesco Amigoni
Sven Behnke
International Symposium on Robotics (ISR)(2010)
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Exploration and mapping are fundamental prerequisites for autonomous robots operating in initially unknown environments. In this paper, we evaluate simple yet efficient frontier-based exploration strategies. Furthermore, we discuss improvements to the classic frontier-based exploration strategy by Yamauchi et al. that further shorten the resulting exploration paths and present results from a comparative evaluation with a reference exploration strategy taken from the literature.
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Sancta Simplicitas - On the efficiency and achievable results of SLAM using ICP-Based Incremental Registration
Sven Behnke
IEEE International Conference on Robotics and Automation (ICRA), Anchorage, Alaska(2010)
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This paper presents an efficient combination of algorithms for SLAM in dynamic environments. The overall approach is based on range image registration using the ICP algorithm. Different extensions to this algorithm are used to incrementally construct point models of the robot’s workspace. A simple heuristic allows for determining which points in a newly acquired range image are already contained in the point model and for adding only those points that provide new information. Furthermore, the means for dealing with environment dynamics are presented which allow for continuously conducting SLAM and updating the point model according to changes in a dynamic environment. The achievable results of the overall approach are compared to Rao-Blackwellized Particle Filters as a state-of-the-art solution to the SLAM problem and evaluated using a recently published benchmark by Burgard et al. (2009).
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Using Time-of-Flight Cameras with Active Gaze Control for 3D Collision Avoidance
David Droeschel
Jörg Stückler
Sven Behnke
IEEE International Conference on Robotics and Automation (ICRA), Anchorage, Alaska, USA(2010)
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We propose a 3D obstacle avoidance method for mobile robots. Besides the robot’s 2D laser range finder, a Timeof-Flight camera is used to perceive obstacles that are not in the scan plane of the laser range finder. Existing approaches that employ Time-of-Flight cameras suffer from the limited field-of-view of the sensor. To overcome this issue, we mount the camera on the head of our anthropomorphic robot Dynamaid. This allows to change the gaze direction through the robot’s pan-tilt neck and its torso yaw joint. The proposed obstacle detection method is robust against kinematic inaccuracies and noise in the range measurements. The gaze controller takes motion blur effects into account and controls the gaze depending on the robot’s motion and the obstacles in its vicinity. In experiments, we demonstrate that our approach enables the robot to avoid obstacles that the laser range finder can not perceive. We also compare our active gaze control strategy with a fixed gaze orientation.
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Probabilistic Phase Unwrapping for Time-of-Flight Cameras
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Time-of-Flight (ToF) cameras gain depth information by emitting amplitude-modulated near-infrared light and measuring the phase shift between the emitted and the reflected signal. The phase shift is proportional to the object’s distance modulo the wavelength of the modulation frequency. This results in a distance ambiguity. Distances larger than the wavelength are wrapped into the sensor’s non-ambiguity range and cause spurious distance measurements. We apply Phase Unwrapping to reconstruct these wrapped measurements. Our approach is based on a probabilistic graphical model using loopy belief propagation to detect and infer the position of wrapped measurements. In experiments, we show that wrapped measurements are identified and corrected allowing to reconstruct the structure of the scene.
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Multi-frequency Phase Unwrapping for Time-of-Flight Cameras
David Droeschel
Sven Behnke
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Taipei, Taiwan(2010)
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Time-of-Flight (ToF) cameras gain depth information by emitting amplitude-modulated near-infrared light and measuring the phase shift between the emitted and the reflected signal. The phase shift is proportional to the object’s distance modulo the wavelength of the modulation frequency. This results in a distance ambiguity. Distances larger than the wavelength are wrapped into the sensor’s non-ambiguity range and cause spurious distance measurements. We apply Phase Unwrapping to reconstruct these wrapped measurements. Our approach is based on a probabilistic graphical model. We use loopy belief propagation to detect and infer the position of wrapped measurements. Besides depth discontinuities, our method utilizes multiple modulation frequencies to identify wrapped measurements. In experiments, we show that wrapped measurements are identified and corrected, even in situations where the scene shows steep slopes in the depth measurements.
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Rescue Robots at the Collapse of the Municipal Archive of Cologne City: a Field Report
Torsten Linder
Viatcheslav Tretyakov
Sebastian Blumenthal
Peter Molitor
Hartmut Surmann
Robin Murphy
Satoshi Tadokoro
IEEE International Workshop on Safety, Security, and Rescue Robotics (SSRR), Bremen, Germany.(2010)
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This paper presents a field report and summarizes the problems of the appliance of rescue robots during the Collapse of the Historical Archive of the City of Cologne. Two robots where on the field, ready to be applied: A shoe-box size tracked mobile robot (VGTV Xtreme) and a caterpillar like system (Active Scope Camera). Due to the special type of collapse and design limitations of the robots, both robotic systems could not be applied. Either they could not reach/fit into voids or could not be controlled from a safe distance. The problems faced have been analyzed and are described in this paper.
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Fast 3D Perception for Collision Avoidance and SLAM in Domestic Environments
David Droeschel
Sven Behnke
Stefan May
Hartmut Surmann
Mobile Robots Navigation(2010), pp. 53-84
Robust Ego-Motion Estimation with ToF Cameras
David Dröschel
Stefan May
Paul Plöger
Sven Behnke
European Conference on Mobile Robots (ECMR), Dubrovnik, Croatia(2009)
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This paper presents an approach to estimate the ego-motion of a robot while moving. The employed sensor is a Time-of-Flight (ToF) camera, the SR3000 from Mesa Imaging. ToF cameras provide depth and reflectance data of the scene at high frame rates. The proposed method utilizes the coherence of depth and reflectance data of ToF cameras by detecting image features on reflectance data and estimating the motion on depth data. The motion estimate of the camera is fused with inertial measurements to gain higher accuracy and robustness. The result of the algorithm is benchmarked against reference poses determined by matching accurate 2D range scans. The evaluation shows that fusing the pose estimate with the data from the IMU improves the accuracy and robustness of the motion estimate against distorted measurements from the sensor.
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Robust and Computationally Efficient Navigation in Domestic Environments
Gerhard K. Kraetzschmar
Erich Rome
Robot Soccer World Cup XIII, Lecture Notes in Computer Science(2009), pp. 104-115
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Presented in this paper is a complete system for robust autonomous navigation in cluttered and dynamic environments. It consists of computationally efficient approaches to the problems of simultaneous localization and mapping, path planning, and motion control, all based on a memory-efficient environment representation. These components have been implemented and integrated with additional components for human-robot interaction and object manipulation on a mobile manipulation platform for service robot applications. The resulting system performed very successfully in the 2008 RoboCup@Home competition.
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Robust 3D-Mapping with Time-of-Flight Cameras
Stefan May
Stefan Fuchs
David Droeschel
Andreas Nüchter
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), St. Louis, Missouri, USA,(2009)
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Time-of-Flight cameras constitute a smart and fast technology for 3D perception but lack in measurement precision and robustness. The authors present a comprehensive approach for 3D environment mapping based on this technology. Imprecision of depth measurements are properly handled by calibration and application of several filters. Robust registration is performed by a novel extension to the Iterative Closest Point algorithm. Remaining registration errors are reduced by global relaxation after loop-closure and surface smoothing. A laboratory ground truth evaluation is provided as well as 3D mapping experiments in a larger indoor environment.
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Three-Dimensional Mapping with Time-of-Flight Cameras
Stefan May
David Droeschel
Stefan Fuchs
Ezio Malis
Andreas Nüchter
Joachim Hertzberg
Journal of Field Robotics, 26(11--12)(2009), pp. 934-965
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This article investigates the use of time‐of‐flight (ToF) cameras in mapping tasks for autonomous mobile robots, in particular in simultaneous localization and mapping (SLAM) tasks. Although ToF cameras are in principle an attractive type of sensor for three‐dimensional (3D) mapping owing to their high rate of frames of 3D data, two features make them difficult as mapping sensors, namely, their restricted field of view and influences on the quality of range measurements by high dynamics in object reflectivity; in addition, currently available models suffer from poor data quality in a number of aspects. The paper first summarizes calibration and filtering approaches for improving the accuracy, precision, and robustness of ToF cameras independent of their intended usage. Then, several ego motion estimation approaches are applied or adapted, respectively, in order to provide a performance benchmark for registering ToF camera data. As a part of this, an extension to the iterative closest point algorithm has been developed that increases the robustness under restricted field of view and under larger displacements. Using an indoor environment, the paper provides results from SLAM experiments using these approaches in comparison. It turns out that the application of ToF cameras is feasible to SLAM tasks, although this type of sensor has a complex error characteristic.
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Continuous 3D Sensing for Navigation and SLAM in Cluttered and Dynamic Environments
Christopher Lörken
Hartmut Surmann
International Conference on Information Fusion (FUSION), Cologne, Germany(2008)
Teleoperated Visual Inspection and Surveillance with Unmanned Ground and Aerial Vehicles
Hartmut Surmann
Sebastian Blumenthal
Thorsten Linder
Peter Molitor
Viatcheslav Tretyakov
International Journal of Online Engineering (iJOE), 4(4)(2008), pp. 26-38
Continuous 3D Environment Sensing for Autonomous Robot Navigation and Mapping
Christopher Lörken
Fachwissenschaftlicher Informatik-Kongress, Lecture Notes in Informatics (LNI)(2007), pp. 39-42
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Presented here is a novel approach for continuously sensing dynamic indoor environments in 3D. Based on this procedure virtual 2D maps are introduced that allow for computationally efficient navigation algorithms. Additionally, a methodology is proposed to interpret the gathered information in a way applicable for prevailing 2D and 3D mapping algorithms.
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