Segmented bundle adjustment algorithm for underwater vision SLAM

doi:10.13474/j.cnki.11-2246.2024.1102

Abstract

Abstract: Autonomous underwater vehicle (AUV) can achieve close-range accurate positioning by using visual SLAM system,but when facing large-scale underwater scenes,the back-end optimization using bundle adjustment(BA) algorithm has the problems of insufficient memory and low computational efficiency. To solve these problems,an improved segmented BA optimization algorithm is proposed. A segmentation method based on motion pattern is used to segment the trajectory according to the straight motion and turning motion of the camera,and then BA optimization is performed on each sub-segment respectively. Each sub-segment is solved by dynamically adjusting the optimization weight,and the optimization parameters are dynamically adjusted according to the motion patterns of different sub-segments. For the solving of BA cost function,the improved Levenberg-Marquadt(L-M) algorithm is adopted,the trust region is defined as the tunable parameter,which reduces the non-convergence problem caused by the singularity of the Jacobian matrix and improves the operation efficiency. According to the experimental results on the dataset,the proposed algorithm has better accuracy than the ORB-SLAM3 algorithm when it runs for a long time and the environment is harsh,and the efficiency of the global BA is significantly improved.

Key words: underwater visual SLAM, back-end optimization, bundle adjustment, segment BA, L-M algorithm

CLC Number:

P237

BAI Yunpeng, XU Huixi, Lü Fengtian. Segmented bundle adjustment algorithm for underwater vision SLAM[J]. Bulletin of Surveying and Mapping, 2024, 0(11): 7-12.

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