HAC-SLAM: Human Assisted Collaborative 3D-SLAM through Augmented Reality

Simultaneous Localization and Mapping (SLAM) has emerged as a prime autonomous mobile agent localization algorithm. Despite the global research effort to improve SLAM, its mapping component remains limited and serves little more than to satisfy the coupled localization problem. We present a collaborative 3D SLAM approach leveraging the power of augmented reality (AR). The system introduces a trio of diverse agents, each with its unique capability to become an active member in the mapping process: mobile robots, human operators, and AR head-mounted display (AR-HMD). A 3D complementary mapping pipeline is developed to utilize the built-in SLAM capabilities of the AR-HMD as shareable data. Our system aligns and merges the AR-HMD and the robot’s local map automatically, triggered by a human-dictated initial guess. The created merged map proves advantageous in scenarios where the robot is restricted from navigating in certain areas. To correct map imperfections resulting from problematic objects such as transparent or reflective surfaces, the fused map is overlayed onto the environment, and hand gestures are used to add or delete 3D map features in real-time. Our system is implemented in both a lab and a real industrial warehouse setup. The results show a significant improvement in the map quality and mapping duration.