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Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

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Underwater Localization and Mapping Based on Multi-Beam Forward Looking Sonar.

Chensheng Cheng¹, Can Wang¹, Dianyu Yang¹

¹School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an, China.

Frontiers in Neurorobotics

|January 31, 2022

Summary

This summary is machine-generated.

This study introduces a new filter-based method for real-time underwater Simultaneous Localization And Mapping (SLAM) using Autonomous Underwater Vehicles (AUVs). The approach effectively processes sonar data for improved navigation and mapping in challenging aquatic environments.

Keywords:

SLAM grid map multi-beam forward looking sonar point cloud underwater vehicle

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Area of Science:

Robotics
Marine Engineering
Computer Vision

Background:

Simultaneous Localization And Mapping (SLAM) is crucial for Autonomous Underwater Vehicle (AUV) navigation.
Real-time underwater SLAM is challenging for small AUVs due to large sonar data volumes and equipment latency.

Purpose of the Study:

To develop a filter-based methodology for efficient underwater SLAM on small AUVs.
To address computational challenges posed by extensive sonar data.

Main Methods:

Utilized a multi-beam forward looking sonar (MFLS) for feature extraction.
Converted sonar images to sparse point clouds via threshold segmentation and distance-constrained filtering.
Fused data from DVL, IMU, and sonar using a Rao-Blackwellized particle filter (RBPF)-based SLAM algorithm.

Main Results:

Successfully estimated AUV pose and generated an occupancy grid map.
Demonstrated improved performance in state estimation compared to existing methods.
Showcased effective suppression of divergence in SLAM computations.

Conclusions:

The proposed filter-based SLAM methodology enhances real-time navigation and mapping for AUVs in underwater environments.
The data processing and fusion techniques effectively mitigate computational burdens and improve accuracy.