WAIS: Leveraging WiFi for Resource-Efficient SLAM

摘要

Interest in autonomous navigation and exploration for indoor applications has spurred research into indoor Simultaneous Localization and Mapping (SLAM) robot systems. While most of these SLAM systems use camera and LiDAR sensors in tandem with an odometry sensor, these odometry sensors drift over time. Visual (LiDAR/camera-based) SLAM systems deploy compute and memory-intensive search algorithms to detect 'Loop Closures' to combat this drift, making the trajectory estimate globally consistent. Instead, WAIS (WiFi Assisted Indoor SLAM) demonstrates using WiFi-based sensing can reduce this resource intensiveness drastically. By covering over 1500 m in realistic indoor environments and WiFi deployments, we showcase 4.3× and 4× reduction in compute and memory consumption compared to state-of-the-art Visual and Lidar SLAM systems. Incorporating WiFi into the sensor stack improves the resiliency of the Visual-SLAM system. We find the 90th percentile translation errors improve by ~ 40% and orientation errors by ~ 60% compared with purely camera-based systems. Additionally, we open-source a toolbox, WiROS, to furnish online and compute efficient WiFi measurements.