LOG-LIO: A LiDAR-Inertial Odometry With Efficient Local Geometric Information Estimation
Author:
Affiliation:
1. School of Surveying and Geo-Informatics, Tongji University, Shanghai, China
2. Department of Computer Science and Technology, School of Electronics and Information Engineering, Tongji University, Shanghai, China
Funder
National Key Research and Development Program of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Artificial Intelligence,Control and Optimization,Computer Science Applications,Computer Vision and Pattern Recognition,Mechanical Engineering,Human-Computer Interaction,Biomedical Engineering,Control and Systems Engineering
Link
http://xplorestaging.ieee.org/ielx7/7083369/10320104/10314726.pdf?arnumber=10314726
Reference20 articles.
1. iSAM2: Incremental smoothing and mapping using the Bayes tree
2. Generalized-ICP
3. Efficient and Probabilistic Adaptive Voxel Mapping for Accurate Online LiDAR Odometry
4. Pixel-Level Extrinsic Self Calibration of High Resolution LiDAR and Camera in Targetless Environments
5. LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping
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