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Cloud-Edge Collaborative Submap-Based VSLAM Using Implicit Representation Transmission
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AI
DOI：10.1109/TVT.2024.3412133.png)
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Abstract
En 中文
Applying VSLAM to mobile robots with limited computing power is the key to achieving autonomous navigation, and the cloud-edge collaborative VSLAM is a solution. However, the VSLAM data transmission in the working site with limited communication is still an open problem. To solve this problem, two core issues should be considered: the transmission frequency and the transmission data volume. In this paper, we propose an asynchronous submap building framework to reduce the transmission frequency. Also, we design an implicit representation-based transmission method to save the transmission data volume while satisfying the data association between the edge and the cloud. Through the experiments, our method shows advanced performance in terms of communication demand with low transmission frequency and small data volume. At the same time, comparable precision to the state-of-the-art is achieved, showing the effectiveness of the data association building. Thanks to the reduced transmission frequency and data volume, our system provides a feasible way to the cloud-based VSLAM under limited communication.
Keywords:
Cloud computing
Collaboration
Robots
Merging
Task analysis
Distributed databases
Buildings
Edge-cloud collaboration
implicit representation
multiple submap VSLAM
Journal
IF:
7.1
Papers:
1.8W
Citations:
6.6W