Radar Localization and Mapping for Indoor Disaster Environments via Multimodal Registration to Prior LiDAR Map This paper presents a localization and mapping algorithm that leverages a radar system in lowvisibility 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 lowvisibility environments; however, radar measurementsxe2x80x99 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 radarmap refinement for mapping. Our proposed method overcomes the significant density discrepancy between LiDAR and radar with a densityindependent point registration algorithm. We validate the proposed method in an environment containing dense fog.

MultiSensor 6DoF Localization For Aerial Robots In Complex GNSSDenied Environments The need for robots autonomously navigating in more and more complex environments has motivated intense Ru0026 D efforts in making robot pose estimation more accurate and reliable. This paper presents a multisensor multihypothesis method for robust 6DoF localization in complex environments. Robustness and accuracy requirements are addressed as follows. First, camera and LIDAR features are seamlessly integrated in the same statistical framework, benefiting from their synergies and providing robustness in scenarios with low or varying densities of LIDAR and visual features. Second, a multihypothesis approach is adopted to cope with scenario symmetries. The method has been carefully designed to operate in real time using feature and hypothesis filtering and efficient hypothesis refinement, and has been coded in a multicore implementation. The proposed method has been extensively validated for closedloop aerial robot navigation in different urban and industrial scenarios and has shown advantages over wellknown singlesensor techniques.