We present a novel FastSLAM approach for a robotic system inspecting structures made of large metal plates. By taking advantage of the reflections of ultrasonic guided waves on the plate boundaries, it is possible to recover, with enough precision, both the plate shape and the robot trajectory. Contrary to our previous work, this approach takes into account the dispersive nature of guided waves in metal plates. This is leveraged to construct beamforming maps from which we solve the mapping problem through plate edges estimation for every particle, in a FastSLAM fashion. It will be demonstrated, with real acoustic measurements obtained on different metal plates, that such a framework achieves better results in terms of convergence and accuracy, while the complexity of the algorithm is sensibly reduced.

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 demonstrate 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 test metal plates using lawnmower paths and random-walks. To the contrary of related works, this paper focuses on the practical details of the localization and mapping algorithm.

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.

In this work 4 , we propose a method based on a particle filter for the localization of an industrial robot on a large metal structure that leverages first-order reflections of acoustic waves on a metal plate edges. In our approach, the acoustic measurements are acquired in a (pseudo) pulse-echo mode using a co-localized emitter/receiver pair of piezoelectric transducers, and we assume a known size of the metal plate. To validate the method, the acquisition of acoustic data is made manually, but it is aimed to be performed by a robotic platform soon. The results demonstrate that with our approach, it is possible to recover the robot localization to a precision of a few millimeters.

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