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- W3201923677 abstract "Field robotics in perceptually-challenging environments require fast and accurate state estimation, but modern LiDAR sensors quickly overwhelm current odometry algorithms. To this end, this letter presents a lightweight frontend LiDAR odometry solution with consistent and accurate localization for computationally-limited robotic platforms. Our Direct LiDAR Odometry (DLO) method includes several key algorithmic innovations which prioritize computational efficiency and enables the use of dense, minimally-preprocessed point clouds to provide accurate pose estimates in real-time. This is achieved through a novel keyframing system which efficiently manages historical map information, in addition to a custom iterative closest point solver for fast point cloud registration with data structure recycling. Our method is more accurate with lower computational overhead than the current state-of-the-art and has been extensively evaluated in multiple perceptually-challenging environments on aerial and legged robots as part of NASA JPL Team CoSTAR’s research and development efforts for the DARPA Subterranean Challenge." @default.
- W3201923677 created "2021-10-11" @default.
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- W3201923677 date "2022-04-01" @default.
- W3201923677 modified "2023-10-18" @default.
- W3201923677 title "Direct LiDAR Odometry: Fast Localization With Dense Point Clouds" @default.
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- W3201923677 doi "https://doi.org/10.1109/lra.2022.3142739" @default.
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