A-SLAM: Human in-the-loop Augmented SLAM

Abstract

In this work, we are proposing an intuitive Augmented SLAM method (A-SLAM) that allows the user to interact, in real-time, with a robot running SLAM to correct for pose and map errors. We built an AR application that works on HoloLens and allows the operator to view the robot's map superposed on the physical environment and edit it. Through map editing, the operator can account for errors affecting real environment's representation by adding navigation-forbidden areas to the map in addition to the ability to correct errors affecting the localization. The proposed system allows the operator to edit the robot's pose (based on SLAM request) and can be extended to sending navigation goals to the robot, viewing the planned path to evaluate it before execution, and teleoperating the robot. The proposed solution could be applied on any 2D-based SLAM algorithm and can easily be extended to 3D SLAM techniques. We validated our system through experimentation on pose correction and map editing. Experiments demonstrated that through A-SLAM, SLAM runtime is cut to half, post-processing of maps is totally eliminated, and high quality occupancy grid maps could be achieved with minimal added computational and hardware costs.