Radar Localization and Mapping for Indoor Disaster Environments via Multi-modal Registration to Prior LiDAR Map

This paper presents a localization and mapping algorithm that leverages a radar system in low-visibility environments. We aim to address disaster situations in which prior knowledge of a place is available from CAD or light detection and ranging (LiDAR) maps, but incoming visibility is severely limited. In smoky environments, typical sensors (e.g., cameras and LiDARs) fail to perform reliably due to the large particles in the air. Radars recently attracted attention for their robust perception in low-visibility environments; however, radar measurements’ angular ambiguity and low resolution prevented the direct application to the simultaneous localization and mapping (SLAM) framework. In this paper, we propose registering radar measurements against a previously built dense LiDAR map for localization and applying radar-map refinement for mapping. Our proposed method overcomes the significant density discrepancy between LiDAR and radar with a density-independent point registration algorithm. We validate the proposed method in an environment containing dense fog.