Cloud Learning-Based Meets Edge Model-Based: Robots Don't Need to Build All the Submaps Itself.

In recent years, significant progress has been made in learning-based VSLAM (Visual Simultaneous Localization and Mapping). Cloud-based VSLAM is a promising solution for meeting the computational demands of learning-based methods in mobile robot applications. However, existing cloud-based VSLAM systems face high transmission demands. To address this issue, we propose a cloud-based VSLAM system, offloading the heavy cost of reconstructing challenging images to the cloud using the learning-based method and leaving the light realtime tracking in the edge using the model-based method. By combining the cloud-edge transmission and a multiple submap VSLAM framework, we introduce a rumination-inspired mechanism for asynchronous and distributed submap building. The submap-based framework and proposed down-sampling method help reduce transmission frequency and data volume. We present experimental results that demonstrate the robustness and precision of our cloud-based multiple submap VSLAM system. We also evaluate the runtime performance of communication and computation on a real robot platform, which suggests that the multiple submap VSLAM framework can effectively release computation load while satisfying both robustness and realtime requirements.