Cloud-based realtime robotic Visual SLAM

Prior work has shown that Visual SLAM (VSLAM) algorithms can successfully be used for realtime processing on local robots. As the data processing requirements increase, due to image size or robot velocity constraints, local processing may no longer be practical. Offloading the VSLAM processing to systems running in a cloud deployment of Robot Operating System (ROS) is proposed as a method for managing increasing processing constraints. The traditional bottleneck with VSLAM performing feature identification and matching across a large database. In this paper, we present a system and algorithms to reduce computational time and storage requirements for feature identification and matching components of VSLAM by offloading the processing to a cloud comprised of a cluster of compute nodes. We compare this new approach to our prior approach where only the local resources of the robot were used, and examine the increase in throughput made possible with this new processing architecture.