Pose-graph visual SLAM with geometric model selection for autonomous underwater ship hull inspection

This paper reports the application of vision based simultaneous localization and mapping (SLAM) to the problem of autonomous ship hull inspection by an underwater vehicle. The goal of this work is to automatically map and navigate the underwater surface area of a ship hull for foreign object detection and maintenance inspection tasks. For this purpose we employ a pose-graph SLAM algorithm using an extended information filter for inference. For perception, we use a calibrated monocular camera system mounted on a tilt actuator so that the camera approximately maintains a nadir view to the hull. A combination of SIFT and Harris features detectors are used within a pairwise image registration framework to provide camera-derived relative-pose constraints (modulo scale). Because the ship hull surface can vary from being locally planar to highly three-dimensional (e.g., screws, rudder), we employ a geometric model selection framework to appropriately choose either an essential matrix or homography registration model during image registration. This allows the image registration engine to exploit geometry information at the early stages of estimation, which results in better navigation and structure reconstruction via more accurate and robust camera-constraints. Preliminary results are reported for mapping a 1,300 image data set covering a 30 m by 5 m section of the hull of a USS aircraft carrier. The post-processed result validates the algorithm´s potential to provide in-situ navigation in the underwater environment for trajectory control, while generating a texture-mapped 3D model of the ship hull as a byproduct for inspection.