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Generalized LOAM: LiDAR Odometry Estimation with Trainable Local Geometric Features

By Kohei Honda and others
This paper presents a LiDAR odometry estimation framework called Generalized LOAM. Our proposed method is generalized in that it can seamlessly fuse various local geometric shapes around points to improve the position estimation accuracy compared to the conventional LiDAR odometry and mapping (LOAM) method. To utilize continuous geometric features for... Show more
October 29, 2022
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Generalized LOAM: LiDAR Odometry Estimation with Trainable Local Geometric Features
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