Feature-Based SLAM for Imaging Sonar with Under-Constrained Landmarks

Eric Westman, Akshay Hinduja, Michael Kaess

Abstract: Recent algorithms have demonstrated the feasibility of underwater feature-based SLAM using imaging sonar. But previous methods have either relied on manual feature extraction and correspondence or used prior knowledge of the scene, such as the planar scene assumption. Our proposed system provides a general-purpose method for feature-point extraction and correspondence in arbitrary scenes. Additionally, we develop a method of identifying point landmarks that are likely to be well-constrained and reliably reconstructed. Finally, we demonstrate that while under-constrained landmarks cannot be accurately reconstructed themselves, they can still be used to constrain and correct the sensor motion. These advances represent a large step towards general-purpose, feature-based SLAM with imaging sonar.