A Joint Graph-Based Approach for Simultaneous Underwater Localization and Mapping for AUV Navigation Fusing Bathymetric and Magnetic-Beacon-Observation Data

Abstract

Accurate positioning is the necessary basis for autonomous underwater vehicles (AUV) to perform safe navigation in underwater tasks, such as port environment monitoring, target search, and seabed exploration. The position estimates of underwater navigation systems usually suffer from an error accumulation problem, which makes the AUVs difficult use to perform long-term and accurate underwater tasks. Underwater simultaneous localization and mapping (SLAM) approaches based on multibeam-bathymetric data have attracted much attention for being able to obtain error-bounded position estimates. Two problems limit the use of multibeam bathymetric SLAM in many scenarios. The first is that the loop closures only occur in the AUV path intersection areas. The second is that the data association is prone to failure in areas with gentle topographic changes. To overcome these problems, a joint graph-based underwater SLAM approach that fuses bathymetric and magnetic-beacon measurements is proposed in this paper. In the front-end, a robust dual-stage bathymetric data-association method is used to first detect loop closures on the multibeam bathymetric data. Then, a magnetic-beacon-detection method using Euler-deconvolution and optimization algorithms is designed to localize the magnetic beacons using a magnetic measurement sequence on the path. The loop closures obtained from both bathymetric and magnetic-beacon observations are fused to build a joint-factor graph. In the back-end, a diagnosis method is introduced to identify the potential false factors in the graph, thus improving the robustness of the joint SLAM system to outliers in the measurement data. Experiments based on field bathymetric datasets are performed to test the performance of the proposed approach. Compared with classic bathymetric SLAM algorithms, the proposed algorithm can improve the data-association accuracy by 50%, and the average positioning error after optimization converges to less than 10 m.