π-Map: A Decision-Based Sensor Fusion with Global Optimization for Indoor Mapping

In this paper, we propose pi-map, a tightly coupled fusion mechanism that dynamically consumes LiDAR and sonar data to generate reliable and scalable indoor maps for autonomous robot navigation. The key novelty of pi-map over previous attempts is the utilization of a fusion mechanism that works in three stages: the first LiDAR scan matching stage efficiently generates initial key localization poses; the second optimization stage is used to eliminate errors accumulated from the previous stage and guarantees that accurate large-scale maps can be generated; then the final revisit scan fusion stage effectively fuses the LiDAR map and the sonar map to generate a highly accurate representation of the indoor environment. We evaluate pi-map on both large and small environments and verify its superiority over existing fusion methods.