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 multi-order 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 multi-order 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.

The ultrasonic mapping of plate-based facilities is an essential step towards the robotic inspection of large metal structures such as storage tanks or ship hulls. This work proposes a novel framework that exploits ultrasonic echoes to recover grid-based and feature-based spatial representations jointly. We aim to improve on a previous mapping method [1] subject to errors due to interference, and which provides plate geometry estimates without uncertainty assessment. The grid can represent, all along the mapping process, both areas identified as inside or outside the current plate and areas whose state is still unknown, making it is suitable e.g. for detecting a change of plate, or for use in a later active-sensing strategy. We also leverage the resulting spatial information to filter out candidate plate edges that are no longer relevant, mitigating the detrimental effect of interference. We test the approach in simulation, with acoustic data acquired manually and with a real robot. Results show that it is effective for building combined map representations and robust to echo misdetection, contrary to a more standard mapping approach.

This paper presents a proof-of-concept for a localization and mapping system for magnetic crawlers performing inspection tasks on structures made of large metal plates. By relying on ultrasonic guided waves reflected from the plate edges, we show that it is possible to recover the plate geometry and robot trajectory to a precision comparable to the signal wavelength. The approach is tested using real acoustic signals acquired on metal plates using lawn-mower paths and random-walks. To the contrary of related works, this paper focuses on the practical details of the localization and mapping algorithm.