Precise Point Set Registration Using Point-to-Plane Distance and Correntropy for LiDAR Based Localization

Guanglin Xu; Shaoyi Du; DIxiao Cui; Sirui Zhang; Badong Chen; Xuetao Zhang; Jianru Xue; Yue Gao

doi:10.1109/IVS.2018.8500525

Precise Point Set Registration Using Point-to-Plane Distance and Correntropy for LiDAR Based Localization

Guanglin Xu
, Shaoyi Du
, DIxiao Cui
, Sirui Zhang
, Badong Chen
, Xuetao Zhang
, Jianru Xue
, Yue Gao

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

4 Scopus citations

Abstract

In this paper, we propose a robust point set registration algorithm which combines correntropy and point-to-plane distance, which can register rigid point sets with noises and outliers. Firstly, as correntropy performs well in handling data with non-Gaussian noises, we introduce it to model rigid point set registration problem based on point-to-plane distance; Secondly, we propose an iterative algorithm to solve this problem, which repeats to compute correspondence and transformation parameters respectively in closed form solutions. Simulated experimental results demonstrate the high precision and robustness of the proposed algorithm. In addition, LiDAR based localization experiments on automated vehicle performs satisfactory for localization accuracy and time consumption.

Original language	English
Title of host publication	2018 IEEE Intelligent Vehicles Symposium, IV 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	734-739
Number of pages	6
ISBN (Electronic)	9781538644522
DOIs	https://doi.org/10.1109/IVS.2018.8500525
State	Published - 18 Oct 2018
Event	2018 IEEE Intelligent Vehicles Symposium, IV 2018 - Changshu, Suzhou, China Duration: 26 Sep 2018 → 30 Sep 2018

Publication series

Name	IEEE Intelligent Vehicles Symposium, Proceedings
Volume	2018-June

Conference

Conference	2018 IEEE Intelligent Vehicles Symposium, IV 2018
Country/Territory	China
City	Changshu, Suzhou
Period	26/09/18 → 30/09/18

Access to Document

10.1109/IVS.2018.8500525

Cite this

Xu, G., Du, S., Cui, DI., Zhang, S., Chen, B., Zhang, X., Xue, J., & Gao, Y. (2018). Precise Point Set Registration Using Point-to-Plane Distance and Correntropy for LiDAR Based Localization. In 2018 IEEE Intelligent Vehicles Symposium, IV 2018 (pp. 734-739). Article 8500525 (IEEE Intelligent Vehicles Symposium, Proceedings; Vol. 2018-June). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IVS.2018.8500525

@inproceedings{6e0ebf6e77d6494885f4cd29e338d799,

title = "Precise Point Set Registration Using Point-to-Plane Distance and Correntropy for LiDAR Based Localization",

abstract = "In this paper, we propose a robust point set registration algorithm which combines correntropy and point-to-plane distance, which can register rigid point sets with noises and outliers. Firstly, as correntropy performs well in handling data with non-Gaussian noises, we introduce it to model rigid point set registration problem based on point-to-plane distance; Secondly, we propose an iterative algorithm to solve this problem, which repeats to compute correspondence and transformation parameters respectively in closed form solutions. Simulated experimental results demonstrate the high precision and robustness of the proposed algorithm. In addition, LiDAR based localization experiments on automated vehicle performs satisfactory for localization accuracy and time consumption.",

author = "Guanglin Xu and Shaoyi Du and DIxiao Cui and Sirui Zhang and Badong Chen and Xuetao Zhang and Jianru Xue and Yue Gao",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE Intelligent Vehicles Symposium, IV 2018 ; Conference date: 26-09-2018 Through 30-09-2018",

year = "2018",

month = oct,

day = "18",

doi = "10.1109/IVS.2018.8500525",

language = "英语",

series = "IEEE Intelligent Vehicles Symposium, Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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booktitle = "2018 IEEE Intelligent Vehicles Symposium, IV 2018",

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Xu, G, Du, S, Cui, DI, Zhang, S, Chen, B , Zhang, X , Xue, J & Gao, Y 2018, Precise Point Set Registration Using Point-to-Plane Distance and Correntropy for LiDAR Based Localization. in 2018 IEEE Intelligent Vehicles Symposium, IV 2018., 8500525, IEEE Intelligent Vehicles Symposium, Proceedings, vol. 2018-June, Institute of Electrical and Electronics Engineers Inc., pp. 734-739, 2018 IEEE Intelligent Vehicles Symposium, IV 2018, Changshu, Suzhou, China, 26/09/18. https://doi.org/10.1109/IVS.2018.8500525

Precise Point Set Registration Using Point-to-Plane Distance and Correntropy for LiDAR Based Localization. / Xu, Guanglin; Du, Shaoyi; Cui, DIxiao et al.
2018 IEEE Intelligent Vehicles Symposium, IV 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 734-739 8500525 (IEEE Intelligent Vehicles Symposium, Proceedings; Vol. 2018-June).

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

TY - GEN

T1 - Precise Point Set Registration Using Point-to-Plane Distance and Correntropy for LiDAR Based Localization

AU - Xu, Guanglin

AU - Du, Shaoyi

AU - Cui, DIxiao

AU - Zhang, Sirui

AU - Chen, Badong

AU - Zhang, Xuetao

AU - Xue, Jianru

AU - Gao, Yue

PY - 2018/10/18

Y1 - 2018/10/18

N2 - In this paper, we propose a robust point set registration algorithm which combines correntropy and point-to-plane distance, which can register rigid point sets with noises and outliers. Firstly, as correntropy performs well in handling data with non-Gaussian noises, we introduce it to model rigid point set registration problem based on point-to-plane distance; Secondly, we propose an iterative algorithm to solve this problem, which repeats to compute correspondence and transformation parameters respectively in closed form solutions. Simulated experimental results demonstrate the high precision and robustness of the proposed algorithm. In addition, LiDAR based localization experiments on automated vehicle performs satisfactory for localization accuracy and time consumption.

AB - In this paper, we propose a robust point set registration algorithm which combines correntropy and point-to-plane distance, which can register rigid point sets with noises and outliers. Firstly, as correntropy performs well in handling data with non-Gaussian noises, we introduce it to model rigid point set registration problem based on point-to-plane distance; Secondly, we propose an iterative algorithm to solve this problem, which repeats to compute correspondence and transformation parameters respectively in closed form solutions. Simulated experimental results demonstrate the high precision and robustness of the proposed algorithm. In addition, LiDAR based localization experiments on automated vehicle performs satisfactory for localization accuracy and time consumption.

UR - https://www.scopus.com/pages/publications/85056757507

U2 - 10.1109/IVS.2018.8500525

DO - 10.1109/IVS.2018.8500525

M3 - 会议稿件

AN - SCOPUS:85056757507

T3 - IEEE Intelligent Vehicles Symposium, Proceedings

SP - 734

EP - 739

BT - 2018 IEEE Intelligent Vehicles Symposium, IV 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2018 IEEE Intelligent Vehicles Symposium, IV 2018

Y2 - 26 September 2018 through 30 September 2018

ER -

Xu G, Du S, Cui DI, Zhang S, Chen B , Zhang X et al. Precise Point Set Registration Using Point-to-Plane Distance and Correntropy for LiDAR Based Localization. In 2018 IEEE Intelligent Vehicles Symposium, IV 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 734-739. 8500525. (IEEE Intelligent Vehicles Symposium, Proceedings). doi: 10.1109/IVS.2018.8500525

Precise Point Set Registration Using Point-to-Plane Distance and Correntropy for LiDAR Based Localization

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