TY - GEN
T1 - Real-Time GICP: Direct LiDAR SLAM for CPU Environment
AU - Fang, Kaiduo
AU - Ho, Ivan Wang Hei
N1 - Funding Information:
*This work was supported in part by the Key-Area Research and Development Program of Guangdong Province (2020B090928001) 1Kaiduo Fang is a PhD student in Department of Electronics and Information Engineering, the Hong Kong Polytechnic University. kaiduo8.fang@connect.polyu.hk 2Ivan Wang-Hei Ho is Associate Professor with the Department of Electronics and Information Engineering, the Hong Kong Polytechnic University. ivanwh.ho@polyu.edu.hk
Publisher Copyright:
© 2022 IEEE.
PY - 2022/11
Y1 - 2022/11
N2 - As a core part in modern Robotics research, Simultaneous Localization and Mapping (SLAM) is widely used in different kinds of autonomous robots, such as autonomous vehicles. Existing direct methods and feature-based methods in LiDAR-SLAM still have different limitations. In this paper, we propose a lightweight, robust and accurate LiDAR-SLAM framework named Real-Time GICP, which contains improved GICP method as front-end and Scan-Context loop detection to construct pose graph optimization. Experimental results show that Real-Time GICP can achieve state-of-the-art accuracy and superior computational efficiency comparing with other open-source GICP methods. Our method can achieve around 50 Hz for 64 rings Velodyne LiDAR with Intel i7 CPU, which makes Real-Time GICP more efficient in when deployed on popular robotics computational platforms.
AB - As a core part in modern Robotics research, Simultaneous Localization and Mapping (SLAM) is widely used in different kinds of autonomous robots, such as autonomous vehicles. Existing direct methods and feature-based methods in LiDAR-SLAM still have different limitations. In this paper, we propose a lightweight, robust and accurate LiDAR-SLAM framework named Real-Time GICP, which contains improved GICP method as front-end and Scan-Context loop detection to construct pose graph optimization. Experimental results show that Real-Time GICP can achieve state-of-the-art accuracy and superior computational efficiency comparing with other open-source GICP methods. Our method can achieve around 50 Hz for 64 rings Velodyne LiDAR with Intel i7 CPU, which makes Real-Time GICP more efficient in when deployed on popular robotics computational platforms.
KW - Autonomous Driving
KW - LiDAR SLAM
KW - Navigation
KW - Robotics
UR - http://www.scopus.com/inward/record.url?scp=85145650554&partnerID=8YFLogxK
U2 - 10.1109/TENCON55691.2022.9977999
DO - 10.1109/TENCON55691.2022.9977999
M3 - Conference article published in proceeding or book
AN - SCOPUS:85145650554
T3 - IEEE Region 10 Annual International Conference, Proceedings/TENCON
SP - 1
EP - 6
BT - Proceedings of 2022 IEEE Region 10 International Conference, TENCON 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE Region 10 International Conference, TENCON 2022
Y2 - 1 November 2022 through 4 November 2022
ER -