Point Cloud Preprocessing on 3D LiDAR data for Unmanned Surface Vehicle in Marine Environment

Xianzhi Qi; Wenxing Fu; Pei An; Bingli Wu; Jie Ma

doi:10.1109/ICIBA50161.2020.9277346

Point Cloud Preprocessing on 3D LiDAR data for Unmanned Surface Vehicle in Marine Environment

Xianzhi Qi
, Wenxing Fu
, Pei An
, Bingli Wu
, Jie Ma

Huazhong University of Science and Technology
Science and Technology on Space Intelligent Control Laboratory

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

9 Scopus citations

Abstract

Point cloud preprocessing is still a challenging task in the marine environment, for it is difficult to filter out non-obstacle points while avoiding damage to the obstacle completeness. In this paper, we propose a novel data preprocessing method on 3D LiDAR data for the unmanned surface vehicle in the marine environment. It consists of two tasks: outlier removal and wake filtering. As the spatial resolution of LiDAR changes with distance, we exploit distance normalization on statistical outlier filter for robust outlier removal. Considering the gradient difference between wave wake and obstacle surface near the water, we define and calculate the vertical state of point cloud on the range image to obtain the pre-filtering point set, and then use the RANSAC method to filter out the wake points. Experiments on real data has demonstrated the effectiveness of the proposed algorithm both in terms of feasibility and accuracy.

Original language	English
Title of host publication	Proceedings of 2020 IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2020
Editors	Bing Xu, Kefen Mou
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	983-990
Number of pages	8
ISBN (Electronic)	9781728152240
DOIs	https://doi.org/10.1109/ICIBA50161.2020.9277346
State	Published - 6 Nov 2020
Externally published	Yes
Event	2020 IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2020 - Chongqing, China Duration: 6 Nov 2020 → 8 Nov 2020

Publication series

Name	Proceedings of 2020 IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2020

Conference

Conference	2020 IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2020
Country/Territory	China
City	Chongqing
Period	6/11/20 → 8/11/20

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 14 Life Below Water

Keywords

3D LIDAR Data Preprocessing
Outliers Fitlering
Unmanned Surface Vehicle
Wake Points Removal

Access to Document

10.1109/ICIBA50161.2020.9277346

Cite this

Qi, X., Fu, W., An, P., Wu, B., & Ma, J. (2020). Point Cloud Preprocessing on 3D LiDAR data for Unmanned Surface Vehicle in Marine Environment. In B. Xu, & K. Mou (Eds.), Proceedings of 2020 IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2020 (pp. 983-990). Article 9277346 (Proceedings of 2020 IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICIBA50161.2020.9277346

Qi, Xianzhi ; Fu, Wenxing ; An, Pei et al. / Point Cloud Preprocessing on 3D LiDAR data for Unmanned Surface Vehicle in Marine Environment. Proceedings of 2020 IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2020. editor / Bing Xu ; Kefen Mou. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 983-990 (Proceedings of 2020 IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2020).

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title = "Point Cloud Preprocessing on 3D LiDAR data for Unmanned Surface Vehicle in Marine Environment",

abstract = "Point cloud preprocessing is still a challenging task in the marine environment, for it is difficult to filter out non-obstacle points while avoiding damage to the obstacle completeness. In this paper, we propose a novel data preprocessing method on 3D LiDAR data for the unmanned surface vehicle in the marine environment. It consists of two tasks: outlier removal and wake filtering. As the spatial resolution of LiDAR changes with distance, we exploit distance normalization on statistical outlier filter for robust outlier removal. Considering the gradient difference between wave wake and obstacle surface near the water, we define and calculate the vertical state of point cloud on the range image to obtain the pre-filtering point set, and then use the RANSAC method to filter out the wake points. Experiments on real data has demonstrated the effectiveness of the proposed algorithm both in terms of feasibility and accuracy.",

keywords = "3D LIDAR Data Preprocessing, Outliers Fitlering, Unmanned Surface Vehicle, Wake Points Removal",

author = "Xianzhi Qi and Wenxing Fu and Pei An and Bingli Wu and Jie Ma",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2020 ; Conference date: 06-11-2020 Through 08-11-2020",

year = "2020",

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doi = "10.1109/ICIBA50161.2020.9277346",

language = "英语",

series = "Proceedings of 2020 IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2020",

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Qi, X, Fu, W, An, P, Wu, B & Ma, J 2020, Point Cloud Preprocessing on 3D LiDAR data for Unmanned Surface Vehicle in Marine Environment. in B Xu & K Mou (eds), Proceedings of 2020 IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2020., 9277346, Proceedings of 2020 IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2020, Institute of Electrical and Electronics Engineers Inc., pp. 983-990, 2020 IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2020, Chongqing, China, 6/11/20. https://doi.org/10.1109/ICIBA50161.2020.9277346

Point Cloud Preprocessing on 3D LiDAR data for Unmanned Surface Vehicle in Marine Environment. / Qi, Xianzhi; Fu, Wenxing; An, Pei et al.
Proceedings of 2020 IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2020. ed. / Bing Xu; Kefen Mou. Institute of Electrical and Electronics Engineers Inc., 2020. p. 983-990 9277346 (Proceedings of 2020 IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2020).

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

TY - GEN

T1 - Point Cloud Preprocessing on 3D LiDAR data for Unmanned Surface Vehicle in Marine Environment

AU - Qi, Xianzhi

AU - Fu, Wenxing

AU - An, Pei

AU - Wu, Bingli

AU - Ma, Jie

PY - 2020/11/6

Y1 - 2020/11/6

N2 - Point cloud preprocessing is still a challenging task in the marine environment, for it is difficult to filter out non-obstacle points while avoiding damage to the obstacle completeness. In this paper, we propose a novel data preprocessing method on 3D LiDAR data for the unmanned surface vehicle in the marine environment. It consists of two tasks: outlier removal and wake filtering. As the spatial resolution of LiDAR changes with distance, we exploit distance normalization on statistical outlier filter for robust outlier removal. Considering the gradient difference between wave wake and obstacle surface near the water, we define and calculate the vertical state of point cloud on the range image to obtain the pre-filtering point set, and then use the RANSAC method to filter out the wake points. Experiments on real data has demonstrated the effectiveness of the proposed algorithm both in terms of feasibility and accuracy.

AB - Point cloud preprocessing is still a challenging task in the marine environment, for it is difficult to filter out non-obstacle points while avoiding damage to the obstacle completeness. In this paper, we propose a novel data preprocessing method on 3D LiDAR data for the unmanned surface vehicle in the marine environment. It consists of two tasks: outlier removal and wake filtering. As the spatial resolution of LiDAR changes with distance, we exploit distance normalization on statistical outlier filter for robust outlier removal. Considering the gradient difference between wave wake and obstacle surface near the water, we define and calculate the vertical state of point cloud on the range image to obtain the pre-filtering point set, and then use the RANSAC method to filter out the wake points. Experiments on real data has demonstrated the effectiveness of the proposed algorithm both in terms of feasibility and accuracy.

KW - 3D LIDAR Data Preprocessing

KW - Outliers Fitlering

KW - Unmanned Surface Vehicle

KW - Wake Points Removal

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M3 - 会议稿件

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T3 - Proceedings of 2020 IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2020

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EP - 990

BT - Proceedings of 2020 IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2020

A2 - Xu, Bing

A2 - Mou, Kefen

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2020

Y2 - 6 November 2020 through 8 November 2020

ER -

Qi X, Fu W, An P, Wu B, Ma J. Point Cloud Preprocessing on 3D LiDAR data for Unmanned Surface Vehicle in Marine Environment. In Xu B, Mou K, editors, Proceedings of 2020 IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 983-990. 9277346. (Proceedings of 2020 IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2020). doi: 10.1109/ICIBA50161.2020.9277346

Point Cloud Preprocessing on 3D LiDAR data for Unmanned Surface Vehicle in Marine Environment

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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