CAMs-SLAM: Cloud-based Multi-submap VSLAM for Multi-source Asynchronous Sensing of Biped Climbing Robots

Biped climbing robots have the character of multi-source asynchronous sensing, and they are powerful to replace humans to complete high-altitude and high-risk operations. As the key to autonomous climbing and visual perception, the VSLAM (Visual Simultaneous Localization And Mapping) for biped climbing robots is studied in this paper. Due to the limited computing power of the biped climbing robot, a cloud-based VSLAM system is introduced to offload the high computation. Considering the limited transmission in the high-altitude climbing environment, we propose a submap-based cloud-edge collaboration mechanism, in which a multiple submap VSLAM system is built. To satisfy the multiple source sensing of the biped climbing robot, we design an asynchronous submap building framework. According to the character of VSLAM, the edge-cloud transmission is optimized to reduce the bandwidth. The evaluations have verified our advanced performance in terms of bandwidth cost and tracking precision. Also, the experiment on the real climbing robot demonstrates our feasibility and superiority. Our Cloud-based Asynchronous Multi-submap VSLAM (CAMs-SLAM) system enables biped climbing robots to environment perception and achieves mapping and localization for later autonomous navigation.