王成导师研究小组的学术论文在SCI期刊The Photogrammetric Record上发表

2024年4月17日，以云南省高校资源与环境遥感重点实验室2020级博士研究生冯宝坤为第一作者、聂胜副研究员为通讯作者，王成、王金亮等为共同作者撰写的“A novel method based on a starburst pattern to register UAV and terrestrial lidar point clouds in forest environments”论文在SCI期刊The Photogrammetric Record上发表（Doi: https://doi.org/10.1111/phor.12487）。

该论文以不同平台的树干点云为基础，分别计算了不同平台点云数据的单木位置，然后结合单木位置构建不同平台的星芒图，并利用极坐标旋转和最小二乘实现粗配准；最后通过ICP算法实现精配准。研究采用落叶松、白桦、松树、杨树四种场景的UAV LiDAR和T-LiDAR数据对所提方法进行验证。结果表明其粗配准平均误差为0.157米，精配准平均误差为0.149米。结果均高于已有的研究，为林业机地点云数据配准提供技术支持。该论文得到了国家重点研发（2021YFF0704600）、广西自然科学基金创新团队（2019GXNSFGA245001）、国家自然科学基金（42171352、42201380）等项目的资助。

这是冯宝坤同学博士期间以第一作者发表的第2篇SCI学术论文（详见附录1、2），让我们恭喜冯宝坤同学！希望他再接再厉！也热烈祝贺团队取好成绩！

附录 1 论文相关信息

标题：A novel method based on a starburst pattern to register UAV and terrestrial lidar point clouds in forest environments

作者：Baokun Feng ^{a, b, c}, Sheng Nie ^{b, c,}*, Cheng Wang ^{a, b, c, d}, Jinliang Wang ^a, Xiaohuan Xi ^{b, c}, Haoyu Wang ^d, Jieying Lao ^e, Xuebo Yang ^{b, c}, Dachao Wang ^f, Yiming Chen ^g, Bo Yang ^g

通讯作者：Sheng Nie, niesheng@radi.ac.cn

作者单位：a. Faculty of Geography, Yunnan Normal University, Kunming 650500, China; b. International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China; c. Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, CAS, Beijing 100094, China; d. College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China; e. School of earth sciences, Yunnan University, Kunming 650500, China; f. Tianjin Institute of Surveying and Mapping Co., Lt, Tianjin 300000, China; g. China Forestry Group Corporation, Beijing 100094, China

出版物：The Photogrammetric Record

摘要：Accurate and efficient registration of unmanned aerial vehicle light detection and ranging (UAV LiDAR) and terrestrial LiDAR (T-LiDAR) data is crucial for forest structure parameter extraction. This study proposes a novel method based on a starburst pattern for the automatic registration of UAV LiDAR and T-LiDAR data in forest scenes. It employs density-based spatial clustering of applications with noise (DBSCAN) for individual tree identification, constructs starburst patterns separately from both LiDAR sources, and utilises polar coordinate rotation and matching to achieve coarse registration. Fine registration is achieved using the iterative closest point (ICP) algorithm. Experimental results demonstrate that the starburst-pattern-based method achieves the desired registration accuracy (average coarse registration error of 0.157 m). Further optimization with ICP yields slight improvements with an average fine registration error of 0.149 m. Remarkably, the proposed method is insensitive to the individual tree detection number when exceeding 10, and the tree position error has minimal impact on registration accuracy. Furthermore, our proposed method outperforms two existing methods in T-LiDAR and UAV LiDAR registration over forest environments.

关键字：ICP, Point Cloud registration, Starburst pattern, T-LiDAR, UAV LiDAR

附录 2 冯宝坤博士期间发表论文清单

[2] Feng, B., Nie, S., Wang, C., Wang, J., Xi, X., Wang, H. et al. (2024) A novel method based on a starburst pattern to register UAV and terrestrial lidar point clouds in forest environments. The Photogrammetric Record, 00, 1–20. Available from: https://doi.org/10.1111/phor.12487

[1]Feng, B.; Nie, S.; Wang, C.; Xi, X.; Wang, J.; Zhou, G.; Wang, H. Exploring the Potential of UAV LiDAR Data for Trunk Point Extraction and Direct DBH Measurement. Remote Sens. 2022, 14, 2753. https://doi.org/10.3390/rs14122753

（云南省高校资源与环境遥感重点实验室  供稿）