Pose-graph SLAM Using Multi-order Ultrasonic Echoes and Beamforming for Long-range Inspection Robots

This paper presents a Graph-based Simultaneous Localization And Mapping (GraphSLAM) approach for a robotic system relying on the reflections of ultrasonic guided waves to enable long-range inspection tasks on plate-based metal structures. A measurement model that can leverage multiorder acoustic echoes is introduced for accurate localization, and beamforming is used for mapping the boundaries of individual metal panels. These two elements are subsequently integrated within a nonlinear least squares optimizer to solve the full offline SLAM problem. We experimentally evaluate the potential of this approach in a laboratory environment. We observe the improved localization accuracy of the multiorder echo model compared to a second model, from previous works, that relies solely on first-order echoes. We also show that the proposed approach can yield accurate SLAM results, hence showcasing the standalone capability of ultrasonic-based GraphSLAM for envisioned long-range inspection applications.

• Machine Intelligence