Work

AUV SLAM method based on SO-CFAR and ADT feature extraction

Year: 2024

Type: article

Abstract: Due to the exceptional detection capabilities, the forward-looking sonar could be adopted in simultaneous localization and mapping (SLAM) for autonomous underwater vehicle (AUVs). This paper primarily investigates the application of the factor graph optimization SLAM algorithm based on feature maps in AUV. It achieves this by combining the smallest of constant false alarm rate (SO-CFAR) and adaptive threshold (ADT) to filter noise from the forward-looking sonar and extract feature point clouds. Furthermore, a weighted iterative closest point (WICP) algorithm is employed for feature point registration, which is extracted from the sonar image. The experimental result based on field data demonstrates that the proposed method, with an 8.52% improvement in root mean square error (RMSE) compared with dead reckoning (DR).

Source: Science Progress

DOI: 10.1177/00368504241286969

Authors: Xiaokai Mu, Haiyang Chen, Jiahao Wang, Hongde Qin, Zhongben Zhu

Institution: Harbin Engineering University

Language: English

Topic: Robotics and Sensor-Based Localization

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,650

Funders: National Natural Science Foundation of China, National Key Research and Development Program of China