Abstract
SLAM (Simultaneous Localisation and Mapping) is very important in the task of mapping unknown deep-sea environments. This paper proposes an AUV cluster SLAM algorithm to improve the efficiency of SLAM mapping and navigation. The algorithm includes three main parts: (1) multi-beam sonar image processing algorithm, which detects and eliminates dynamic points while removing redundant information. (2) Combining DVL (Doppler Velocity Log), IMU (Inertial Measurement Unit) and DM (Depth Meter) data, SLAM is performed based on a Rao-Blackwellised particle filter (RBPF ). (3) The innovative iUSBL (inverted ultra-short baseline) system is used to realise the cooperative positioning between the master and slave AUVs. The multi-AUV underwater detection and mapping collaborative SLAM algorithm proposed in this paper not only significantly improves the mapping efficiency in unknown deep-sea environments but also effectively suppresses the errors introduced by dynamic points and ensures stable SLAM performance. Compared with a single AUV, the efficiency of mapping is significantly improved.
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