@inproceedings{8a01103d1f8b434ebd1718ac6e086c78,
title = "Registration method of 3-D laser point cloud data of snow field",
abstract = "Laser point cloud registration is a crucial part of 3D reconstruction. The laser scanning of snow field is prone to noise and the lack of point cloud data leads to poor registration accuracy of point cloud. The optimized particle swarm optimization algorithm must be used to find the optimal spatial transformation matrix point cloud coarse registration method with point cloud distribution entropy as the optimal target, which provides good initial conditions for point cloud fine registration. Through simulation verification, it is proved that the point cloud entropy has faster calculation speed than the traditional mean squared difference evaluation method. The Particle Swarm Optimization based on Beetle Antennae Search Algorithm has the characteristics of strong global search ability and fast calculation speed. It shows better robustness under conditions of noise and missing data.",
keywords = "Lidar, Point Cloud Registration, Snowfield",
author = "Haiyang Zhang and Siqi Chen and Zilong Zhang and Changming Zhao",
note = "Publisher Copyright: Copyright {\textcopyright} 2020 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved; 8th International Conference on Photonics, Optics and Laser Technology, PHOTOPTICS 2020 ; Conference date: 27-02-2020 Through 29-02-2020",
year = "2020",
language = "English",
series = "PHOTOPTICS 2020 - Proceedings of the 8th International Conference on Photonics, Optics and Laser Technology",
publisher = "SciTePress",
pages = "100--106",
editor = "Pablo Albella and Maria Raposo and David Andrews and Paulo Ribeiro",
booktitle = "PHOTOPTICS 2020 - Proceedings of the 8th International Conference on Photonics, Optics and Laser Technology",
}