LOG-LIO: A LiDAR-Inertial Odometry With Efficient Local Geometric Information Estimation-Reference-Cited by-同舟云学术

LOG-LIO: A LiDAR-Inertial Odometry With Efficient Local Geometric Information Estimation

Published:2024-01 Issue:1 Volume:9 Page:459-466
ISSN:2377-3766
Container-title:IEEE Robotics and Automation Letters
language:
Short-container-title:IEEE Robot. Autom. Lett.

Author:

Huang Kai¹^ORCID,Zhao Junqiao²^ORCID,Zhu Zhongyang²^ORCID,Ye Chen²^ORCID,Feng Tiantian¹^ORCID

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

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. LOG-LIO2: A LiDAR-Inertial Odometry With Efficient Uncertainty Analysis;IEEE Robotics and Automation Letters;2024-10

2. An Efficient and Robust Tightly Coupled Framework for Lidar-IMU Localization and Mapping;2023 IEEE International Conference on Robotics and Biomimetics (ROBIO);2023-12-04