Improving Energy Performance of Camera Lidar Fusion by Intermittent Human Detection for Social Navigation

Detection and avoidance of dynamic obstacles is an integral part of social robot navigation. Reliable human detection depends on camera identification, which can be achieved only using computationally expensive algorithms running on a Graphical Processing Unit (GPU). The process is time consuming, causing latency and it cannot be run on low-end systems. Human detection and tracking also requires lidar data fusion to ensure proper localization. In this work, we propose a detection strategy that allows the fusion system to run with lower camera frame rates, and hence decrease latency and computational requirements considerably. We show the effectiveness of the proposed approach in simulation.