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Reducing Communication Consumption in Collaborative Visual SLAM with Map Point Selection and Efficient Data Compression

2023; Springer Science+Business Media; Linguagem: Inglês

10.1007/978-981-99-7590-7_2

1865-0937

Weiqiang Zhang, Lan Cheng, Xinying Xu, Zhimin Hu,

Advanced Image and Video Retrieval Techniques

Efficient data communication is a challenging problem for Collaborative Visual Simultaneous Localization and Mapping (CVSLAM), particularly in bandwidth-limited applications. To resolve this problem, we propose a communication load reduction method. We first propose a map point culling strategy by considering maximum pose-visibility and spatial diversity, to eliminate redundant map information in CVSLAM. Then, we employ a Zstandard (Zstd) compression algorithm to compress visual information so as to reduce the required communication bandwidth. To exhibit the efficiency of the suggested approach, we implement this method in a centralized collaborative monocular SLAM (CCM-SLAM) system. Extensive experimental evaluations indicate that our method can reduce communication overhead by approximately 49% while maintaining map accuracy and real-time performance.