Semantic SLAM for an AUV using object recognition from point clouds

This paper presents a navigation and mapping system for an autonomous underwater vehicle (AUV) while operating near a man-made underwater environment. The objective is to recognize objects (or object parts) and use these as landmarks for simultaneous localization and mapping (SLAM). This approach is intended as the first step towards autonomous object manipulation, to be carried out at a later stage. The approach contains two main components: Object recognition from range data, and feature-based semantic SLAM. For the first component we propose an automatic method for the recognition and location of 3D objects using 3D point clouds as input, extracted from a laser scanner. Since it is common in inspection maintenance and repair (IMR) applications to have access to the 3D models of the objects of interest, the proposed method assumes a priori knowledge of the 3D models of these objects. In typical man-made environments, such objects can be distinct components of a structure, such as valves, pipes and wet-mateable connectors. The object recognition is based on recently proposed global descriptors for point clouds, that allow a compact description of the object shape, which is independent of the object view point. Once an object is recognized, its pose with respect to the AUV is determined using an ICP-based method. The second component of the approach is a feature based SLAM algorithm that uses the recognized objects as landmarks to improve the AUV navigation. The paper presents preliminary results obtained with the Girona 500 AUV, equipped with a fast laser scanner recently developed at the University of Girona. Tests conducted in a controlled environment (water tank) illustrate the suitability of the approach.