CAMs-SLAM: Cloud-Based Multisubmap VSLAM for Multisource Asynchronous Sensing of Biped Climbing Robots

Biped climbing robots (BiCRs) have the character of multisource 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 visual simultaneous localization and mapping (VSLAM) for BiCRs is studied in this article. Due to the limited computing power of the BiCR, 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 BiCR, 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 multisubmap VSLAM (CAMs-SLAM) system enables BiCRs to environment perception and achieves mapping and localization for later autonomous navigation.