Localization on Indoor Topological Maps-SCAM: Scale-Compatible Adaptive Monte-Carlo Localization

This paper introduces Scale Compatible Adaptive MonteCarlo Localization (SCAM) to localize on topological maps, such as hand-drawn maps and floor plans. This enables fast modifications to maps of indoor spaces whereby the layout changes frequently via image editing instead of re-mapping the enviro-nment. SCAM uses x and y scale components in addition to a 2D pose to form a five-dimensional state, modifying Adaptive Monte-Carlo Localization (AMCL) significantly at the prediction and re-sampling step to account for the extra scale components. The scale components influence the projection of the LiDAR scan on each particle, improving the scan match from the LiDAR scan with the imperfect map. The performance of SCAM is tested with real-life data gathered on an empty hallway, a cluttered lab, and a populous lobby. SCAM is evaluated on hand-drawn maps, where the introduction of scale components in SCAM prevented major issues such as loss of localization and running over obstacles, but sees more minor issues such as gliding past obstacles. This result is further verified via repetitions of test cases and making modifications to the map while preserving the topology, and applied to a floor plan. On scaled point cloud maps, SCAM only introduces relatively small positional errors of 0.270m, 0.161m, 0.491m, and heading errors of 1.55 degrees, 8.10 degrees, 10.29 degrees in the three test areas respectively.