TY - GEN
T1 - Downsample-Based Improved Dense Point Cloud Registration Framework
AU - Yang, Shuai
AU - Song, Chunlei
AU - Han, Yongqiang
AU - Chen, Jiabin
AU - Piao, Zhengquan
AU - Wang, Zhenhao
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - In LiDAR-based SLAM algorithms, the process of point cloud registration stands as a pivotal step. When the actual sensor is used to collect point cloud data, the quantity of points within the collected point cloud is frequently immense. Traditional point cloud registration algorithms cannot effectively and quickly handle the registration of dense point clouds. This paper presents a Downsample-based Improved Dense Point Cloud Registration Framework. On the basis of ensuring the registration accuracy, the registration of tens of millions of point clouds can be quickly realized, which saves a lot of time for the entire mapping process. After experimental verification, the algorithm can realize the registration of tens of millions of point clouds within 2 min, which provides a solution for the registration of dense point clouds.
AB - In LiDAR-based SLAM algorithms, the process of point cloud registration stands as a pivotal step. When the actual sensor is used to collect point cloud data, the quantity of points within the collected point cloud is frequently immense. Traditional point cloud registration algorithms cannot effectively and quickly handle the registration of dense point clouds. This paper presents a Downsample-based Improved Dense Point Cloud Registration Framework. On the basis of ensuring the registration accuracy, the registration of tens of millions of point clouds can be quickly realized, which saves a lot of time for the entire mapping process. After experimental verification, the algorithm can realize the registration of tens of millions of point clouds within 2 min, which provides a solution for the registration of dense point clouds.
KW - Dense Point Cloud
KW - Downsample-based
KW - Point Cloud Registration
UR - http://www.scopus.com/pages/publications/105001323293
U2 - 10.1007/978-981-96-2220-7_35
DO - 10.1007/978-981-96-2220-7_35
M3 - Conference contribution
AN - SCOPUS:105001323293
SN - 9789819622191
T3 - Lecture Notes in Electrical Engineering
SP - 354
EP - 365
BT - Advances in Guidance, Navigation and Control - Proceedings of 2024 International Conference on Guidance, Navigation and Control Volume 6
A2 - Yan, Liang
A2 - Duan, Haibin
A2 - Deng, Yimin
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Conference on Guidance, Navigation and Control, ICGNC 2024
Y2 - 9 August 2024 through 11 August 2024
ER -