Sonar-based SLAM using Particle Filter and Free-Space Mapping Approach

This study addresses the sonar perceptual ambiguity problem inherent in underwater sonar-based SLAM systems, resulting from the sonar’s wide beam aperture. We propose a SLAM algorithm that integrates a free-space mapping approach with a particle filter, leveraging polar-based acoustic images collected from an MSIS. Our method utilizes grid maps to compile information on empty regions, aiming at improving mapping accuracy. Free-space representations are further leveraged for particle filter weight update via scan matching. The centre-of-mass map cell representation is exploited for efficient weight update using simple matrix operations. Illustrative experimental results are provided, based on real data collected from a testing pool environment.