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- W3141572377 abstract "Disaster response and search and rescue missions are among the most difficult missions in which an autonomous robot can be deployed. These require a robot to autonomously navigate chaotic, unstructured indoor and outdoor environments. However popular state estimation and mapping methods using vision and lidar are severely handicapped by fog, smoke, or other airborne particulates; conditions common in disaster scenarios. This work presents radar-based methods for state estimation and mapping that are not affected by smoke and fog. We demonstrate the performance of these methods are comparable to other popular methods in favorable conditions. We also demonstrate visual and lidar-based methods degrade quickly in fog, while our methods do not." @default.
- W3141572377 created "2021-04-13" @default.
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- W3141572377 date "2021-01-01" @default.
- W3141572377 modified "2023-09-24" @default.
- W3141572377 title "Radar-Inertial State Estimation and Obstacle Detection for Micro-Aerial Vehicles in Dense Fog" @default.
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- W3141572377 doi "https://doi.org/10.1007/978-3-030-71151-1_1" @default.
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