Reducing Communication Consumption in Collaborative Visual SLAM with Map Point Selection and Efficient Data Compression
Communications in Computer and Information Science Advanced Computational Intelligence and Intelligent Informatics(2023)
摘要
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.
更多查看译文
关键词
collaborative visual slam,map point selection,communication consumption
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要