Underwater Pose SLAM using GMM scan matching for a mechanical profiling sonar

Abstract

The underwater domain is a challenging environment for robotics because widely used electromagnetic devices must be substituted by acoustic equivalents, much slower and noisier. In this paper a two-dimensional pose simultaneous localization and mapping (SLAM) system for an Autonomous Underwater Vehicle based on inertial sensors and a mechanical profiling sonar is presented. Two main systems are specially designed. On the one hand, a dead reckoning system based on Lie Theory is presented to track integrated pose uncertainty. On the other hand, a rigid scan matching technique specialized for acoustic data is proposed, which allows one to estimate the uncertainty of the matching result. Moreover, Bayesian–Gaussian mixtures models are introduced to the scan matching problem and the registration problem is solved by an optimization in Lie groups. The SLAM system is tested on real data and executed in real time with the robotic application. Using this system, section maps at constant depth can be obtained from a three-dimensional underwater domain. The presented SLAM system constitutes the first achievement towards an underwater Active SLAM application