Self-Supervised Learning of End-to-End 3D LiDAR Odometry for Urban Scene Modeling

Self-Supervised Learning of End-to-End 3D LiDAR Odometry for Urban Scene Modeling

Accurate and robust spatial perception is fundamental for dynamic 3D city modeling and urban environmental sensing. High-resolution remote sensing data, particularly LiDAR point clouds, are pivotal for these tasks due to their lighting invariance and precise geometric information. However, processin...

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Bibliographic Details
Main Authors: Shuting Chen, Zhiyong Wang, Chengxi Hong, Yanwen Sun, Hong Jia, Weiquan Liu
Format: Article
Language:English
Published: MDPI AG 2025-08-01
Series:Remote Sensing
Subjects:
LiDAR point cloud
odometry
covariance estimation
deep learning
Online Access:https://www.mdpi.com/2072-4292/17/15/2661
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