Work

Reducing Bandwidth Usage in CVSLAM: A Novel Approach to Map Point Selection and Efficient Data Compression

Year: 2024

Type: article

Abstract: In the field of collaborative visual simultaneous localization and mapping (CVSLAM), efficient data communication poses a significant challenge, particularly in environments with limited bandwidth. To address this issue, we introduce a method aimed at reducing communication consumption. Our approach starts with a strategic culling of map points, aiming at maximizing pose-visibility and expanding spatial diversity to effectively eliminate redundant data in CVSLAM. We achieve this by formulating the problem of maximizing pose-visibility and spatial diversity as a minimum-cost maximum-flow graph optimization problem. Subsequently, we apply finite state entropy encoding for the compression of visual information, further alleviating bandwidth constraints. To verify the proposed method, we implement it within a centralized collaborative monocular simultaneous localization and mapping (SLAM) system. Our approach has been tested on publicly available datasets and in real-world scene. The results show a prominent reduction in bandwidth usage by 49% while maintaining mapping accuracy and without introducing additional latency, confirming its effectiveness in a multi-agent system setting.

Source: Journal of Advanced Computational Intelligence and Intelligent Informatics

DOI: 10.20965/jaciii.2024.p1154

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

Institution: Taiyuan University of Technology

Language: English

Topic: Robotics and Sensor-Based Localization

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: diamond

Funders: National Natural Science Foundation of China, Natural Science Foundation of Shanxi Province