A Fast Iterative Closest Point-Based Autonomous Mobile Robot Localization Method Using Map Preprocessing

Shan He; Nankun Zhao; Tao Song; Zhongxia Xiong; Pengchen Wang; Xinkai Wu

doi:10.1007/978-981-96-3969-4_1

A Fast Iterative Closest Point-Based Autonomous Mobile Robot Localization Method Using Map Preprocessing

Shan He
, Nankun Zhao
, Tao Song
, Zhongxia Xiong
, Pengchen Wang
, Xinkai Wu^*

^*Corresponding author for this work

School of Transportation Science and Engineering

Beihang University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Matching LiDAR scans with map using the Iterative Closest Point (ICP) algorithm is one of the classic methods for robot pose estimation. However, ICP requires finding the nearest reference point in the map for each scan point during each iteration, which is extremely time-consuming. Inspired by the Euclidean Distance Field (EDF), this paper proposes a map preprocessing method to greatly improve the ICP matching efficiency. Firstly, the Breadth First Search (BFS) algorithm is used to find the nearest k obstacle grid points for each non obstacle grid point in the map and store the results in a hash table. Then, the set of nearest neighbors for each scan point during the iteration is obtained by querying the hash table. Finally, the closed form solution of Singular Value Decomposition (SVD) is applied to obtain the pose transformation. Experimental results show that our method significantly improves the computational efficiency of the algorithm while ensuring matching accuracy.

Original language	English
Title of host publication	The Proceedings of 2024 International Conference on Artificial Intelligence and Autonomous Transportation - Volume IV
Editors	Jun Liu, Jianjian Yang, Minyi Xu, Quan Yu, Wenchao Shen
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	1-9
Number of pages	9
ISBN (Print)	9789819639687
DOIs	https://doi.org/10.1007/978-981-96-3969-4_1
State	Published - 2025
Event	International Conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024 - Beijing, China Duration: 6 Dec 2024 → 8 Dec 2024

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	1392 LNEE
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	International Conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024
Country/Territory	China
City	Beijing
Period	6/12/24 → 8/12/24

Keywords

Automated Mobile Robots
Iterative Closest Point
Localization
Scan Matching

Access to Document

10.1007/978-981-96-3969-4_1

Cite this

He, S., Zhao, N., Song, T., Xiong, Z., Wang, P., & Wu, X. (2025). A Fast Iterative Closest Point-Based Autonomous Mobile Robot Localization Method Using Map Preprocessing. In J. Liu, J. Yang, M. Xu, Q. Yu, & W. Shen (Eds.), The Proceedings of 2024 International Conference on Artificial Intelligence and Autonomous Transportation - Volume IV (pp. 1-9). (Lecture Notes in Electrical Engineering; Vol. 1392 LNEE). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-96-3969-4_1

He, Shan ; Zhao, Nankun ; Song, Tao et al. / A Fast Iterative Closest Point-Based Autonomous Mobile Robot Localization Method Using Map Preprocessing. The Proceedings of 2024 International Conference on Artificial Intelligence and Autonomous Transportation - Volume IV. editor / Jun Liu ; Jianjian Yang ; Minyi Xu ; Quan Yu ; Wenchao Shen. Springer Science and Business Media Deutschland GmbH, 2025. pp. 1-9 (Lecture Notes in Electrical Engineering).

@inproceedings{5708923242e7481c9f97f6aaf28916ac,

title = "A Fast Iterative Closest Point-Based Autonomous Mobile Robot Localization Method Using Map Preprocessing",

abstract = "Matching LiDAR scans with map using the Iterative Closest Point (ICP) algorithm is one of the classic methods for robot pose estimation. However, ICP requires finding the nearest reference point in the map for each scan point during each iteration, which is extremely time-consuming. Inspired by the Euclidean Distance Field (EDF), this paper proposes a map preprocessing method to greatly improve the ICP matching efficiency. Firstly, the Breadth First Search (BFS) algorithm is used to find the nearest k obstacle grid points for each non obstacle grid point in the map and store the results in a hash table. Then, the set of nearest neighbors for each scan point during the iteration is obtained by querying the hash table. Finally, the closed form solution of Singular Value Decomposition (SVD) is applied to obtain the pose transformation. Experimental results show that our method significantly improves the computational efficiency of the algorithm while ensuring matching accuracy.",

keywords = "Automated Mobile Robots, Iterative Closest Point, Localization, Scan Matching",

author = "Shan He and Nankun Zhao and Tao Song and Zhongxia Xiong and Pengchen Wang and Xinkai Wu",

note = "Publisher Copyright: {\textcopyright} Beijing Paike Culture Commu. Co., Ltd. 2025.; International Conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024 ; Conference date: 06-12-2024 Through 08-12-2024",

year = "2025",

doi = "10.1007/978-981-96-3969-4\_1",

language = "英语",

isbn = "9789819639687",

series = "Lecture Notes in Electrical Engineering",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "1--9",

editor = "Jun Liu and Jianjian Yang and Minyi Xu and Quan Yu and Wenchao Shen",

booktitle = "The Proceedings of 2024 International Conference on Artificial Intelligence and Autonomous Transportation - Volume IV",

address = "德国",

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He, S, Zhao, N, Song, T, Xiong, Z, Wang, P & Wu, X 2025, A Fast Iterative Closest Point-Based Autonomous Mobile Robot Localization Method Using Map Preprocessing. in J Liu, J Yang, M Xu, Q Yu & W Shen (eds), The Proceedings of 2024 International Conference on Artificial Intelligence and Autonomous Transportation - Volume IV. Lecture Notes in Electrical Engineering, vol. 1392 LNEE, Springer Science and Business Media Deutschland GmbH, pp. 1-9, International Conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024, Beijing, China, 6/12/24. https://doi.org/10.1007/978-981-96-3969-4_1

A Fast Iterative Closest Point-Based Autonomous Mobile Robot Localization Method Using Map Preprocessing. / He, Shan; Zhao, Nankun; Song, Tao et al.
The Proceedings of 2024 International Conference on Artificial Intelligence and Autonomous Transportation - Volume IV. ed. / Jun Liu; Jianjian Yang; Minyi Xu; Quan Yu; Wenchao Shen. Springer Science and Business Media Deutschland GmbH, 2025. p. 1-9 (Lecture Notes in Electrical Engineering; Vol. 1392 LNEE).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - A Fast Iterative Closest Point-Based Autonomous Mobile Robot Localization Method Using Map Preprocessing

AU - He, Shan

AU - Zhao, Nankun

AU - Song, Tao

AU - Xiong, Zhongxia

AU - Wang, Pengchen

AU - Wu, Xinkai

N1 - Publisher Copyright: © Beijing Paike Culture Commu. Co., Ltd. 2025.

PY - 2025

Y1 - 2025

N2 - Matching LiDAR scans with map using the Iterative Closest Point (ICP) algorithm is one of the classic methods for robot pose estimation. However, ICP requires finding the nearest reference point in the map for each scan point during each iteration, which is extremely time-consuming. Inspired by the Euclidean Distance Field (EDF), this paper proposes a map preprocessing method to greatly improve the ICP matching efficiency. Firstly, the Breadth First Search (BFS) algorithm is used to find the nearest k obstacle grid points for each non obstacle grid point in the map and store the results in a hash table. Then, the set of nearest neighbors for each scan point during the iteration is obtained by querying the hash table. Finally, the closed form solution of Singular Value Decomposition (SVD) is applied to obtain the pose transformation. Experimental results show that our method significantly improves the computational efficiency of the algorithm while ensuring matching accuracy.

AB - Matching LiDAR scans with map using the Iterative Closest Point (ICP) algorithm is one of the classic methods for robot pose estimation. However, ICP requires finding the nearest reference point in the map for each scan point during each iteration, which is extremely time-consuming. Inspired by the Euclidean Distance Field (EDF), this paper proposes a map preprocessing method to greatly improve the ICP matching efficiency. Firstly, the Breadth First Search (BFS) algorithm is used to find the nearest k obstacle grid points for each non obstacle grid point in the map and store the results in a hash table. Then, the set of nearest neighbors for each scan point during the iteration is obtained by querying the hash table. Finally, the closed form solution of Singular Value Decomposition (SVD) is applied to obtain the pose transformation. Experimental results show that our method significantly improves the computational efficiency of the algorithm while ensuring matching accuracy.

KW - Automated Mobile Robots

KW - Iterative Closest Point

KW - Localization

KW - Scan Matching

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DO - 10.1007/978-981-96-3969-4_1

M3 - 会议稿件

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SN - 9789819639687

T3 - Lecture Notes in Electrical Engineering

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BT - The Proceedings of 2024 International Conference on Artificial Intelligence and Autonomous Transportation - Volume IV

A2 - Liu, Jun

A2 - Yang, Jianjian

A2 - Xu, Minyi

A2 - Yu, Quan

A2 - Shen, Wenchao

PB - Springer Science and Business Media Deutschland GmbH

T2 - International Conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

Y2 - 6 December 2024 through 8 December 2024

ER -

He S, Zhao N, Song T, Xiong Z, Wang P, Wu X. A Fast Iterative Closest Point-Based Autonomous Mobile Robot Localization Method Using Map Preprocessing. In Liu J, Yang J, Xu M, Yu Q, Shen W, editors, The Proceedings of 2024 International Conference on Artificial Intelligence and Autonomous Transportation - Volume IV. Springer Science and Business Media Deutschland GmbH. 2025. p. 1-9. (Lecture Notes in Electrical Engineering). doi: 10.1007/978-981-96-3969-4_1

A Fast Iterative Closest Point-Based Autonomous Mobile Robot Localization Method Using Map Preprocessing

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