@inproceedings{6f3b89444aa342d4b22c9708c0a5bd12,
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",
month = nov,
day = "6",
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",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "983--990",
editor = "Bing Xu and Kefen Mou",
booktitle = "Proceedings of 2020 IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2020",
}
