面向采样间隔未知的地面激光扫描点云数据，提出了一种使用随机邻域分析的采样间隔估算方法。在扫描数据中随机选取n个中心点并构建k邻域，计算每个中心点光束与相应邻域点光束的水平和竖直方向夹角；以区间宽度△统计水平和竖直夹角数目并构建直方图，取点数第2多的区间内的夹角均值作为当前△下的水平和竖直采样间隔；在此基础上，以多个△参数得到的采样间隔中位数作为最终估算结果。该文在不同扫描仪采集的数据上进行多组实验，并在MathWorks平台上共享实验代码。实验结果表明，所提方法的估算误差小于0.002°，对不同地物类型和参数设置具有很好的稳定性。
This paper focuses on the estimation of sampling interval from terrestrial laser scanning data and presents an estimation method based on the neighboring analysis of randomly selected points. We select n central points from the randomly scanning data and search for k nearest points of each central point. The horizontal and vertical (with Z axis) angles between the laser beam of each central point and corresponding neighboring points are calculated. Then histograms of horizontal and vertical angles are constructed respectively, with interval step △. The average angle of the interval with the second largest number of points is recognized as the corresponding horizontal or vertical sampling interval. Finally, the median value of the horizontal or vertical sampling values generated from a series of △ values is used as the final estimation result. Tests are carried on the data obtained from different scanners and test code is shared on the MathWorks community. The test results show that the error of our method is smaller than 0.002° with good robustness with respect to different types of targets and parameter settings.
[1] 刘亚文, 覃苏舜. 点云数据稀疏区域建筑物立面重建方法[J]. 应用科学学报, 2017, 35(2):217-225. Liu Y W, Tan S S. Building facade reconstruction on sparse LiDAR data region[J]. Journal of Applied Sciences, 2017, 35(2):217-225. (in Chinese)
[2] Chen M L, Wan Y C, Wang M W, et al. Automatic stem detection in terrestrial laser scanning data with distance-adaptive search radius[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(5):2968-2979.
[3] 郑晓敏, 沈健, 祖文迪. 三维激光扫描在砖结构古墓葬数字化工程中的应用[J]. 测绘通报, 2020(增刊 1):192-194, 209. Zheng X M, Shen J, Zu W D. Application of 3D laser scan technique in digitized project of heritage brick tomb[J]. Bulletin of Surveying and Mapping, 2020(Supple.1):192-194, 209. (in Chinese)
[4] Lichti D D, Jamtsho S. Angular resolution of terrestrial laser scanners[J]. The Photogrammetric Record, 2006, 21(114):141-160.
[5] 杨荣华, 花向红, 邱卫宁, 等. 地面三维激光扫描点云角度分辨率研究[J]. 武汉大学学报(信息科学版), 2012, 37(7):851-853. Yang R H, Hua X H, Qiu W N, et al. Research on the point cloud angular resolution of terrestrial laser scanners[J]. Geomatics and Information Science of Wuhan University, 2012, 37(7):851-853. (in Chinese)
[6] 陈西江, 花向红, 邱卫宁, 等. 点云角度分辨率精度评定[J]. 武汉大学学报(信息科学版), 2013, 38(9):1044-1047. Chen X J, Hua X H, Qiu W N, et al. Accuracy evaluation of point cloud angular resolution[J]. Geomatics and Information Science of Wuhan University, 2013, 38(9):1044-1047. (in Chinese)
[7] Chen X, Yu K, Zhang G, et al. Precision estimation of the angular resolution of terrestrial laser scanners[J]. The Photogrammetric Record, 2017, 32(159):276-290.
[8] 朱凌, 石若明. 地面三维激光扫描点云分辨率研究[J]. 遥感学报, 2008, 12(3):405-410. Zhu L, Shi R M. Research on the point cloud resolutions of TLS[J]. Journal of Remote Sensing, 2008, 12(3):405-410. (in Chinese)
[9] 史文中, 李必军, 李清泉. 基于投影点密度的车载激光扫描距离图像分割方法[J]. 测绘学报, 2005(2):95-100. Shi W Z, Li B J, Li Q Q. A method for segmentation of range image captured by vehicle-borne laserscanning based on the density of projected points[J]. Acta Geodaetica et Cartographica Sinica, 2005(2):95-100. (in Chinese)
[10] Cheng L, Tong L, Li M, et al. Semi-automatic registration of airborne and terrestrial laser scanning data using building corner matching with boundaries as reliability check[J]. Remote Sensing, 2013, 5(12):6260-6283.
[11] Cheng L, Tong L, Wu Y, et al. Shiftable leading point method for high accuracy registration of airborne and terrestrial LiDAR data[J]. Remote Sensing, 2015, 7(2):1915-1936.
[12] Cheng X, Cheng X, Li Q, et al. Automatic registration of terrestrial and airborne point clouds using building outline features[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018:1-11.
[13] Chen M, Pan J, Xu J. Classification of terrestrial laser scanning data with density-adaptive geometric features[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15(11):1-5.
[14] Liu K, Wang W, Tharmarasa R, et al. Dynamic vehicle detection with sparse point clouds based on PE-CPD[J]. IEEE Transactions on Intelligent Transportation Systems, 2018:1-14.
[15] 张志刚, 孙立才, 汪沛. 基于激光扫描技术的行人检测方法研究[J]. 计算机科学, 2016, 43(7):328-331. Zhang Z G, Wang L C, Wang P. Research on pedestrian detection method based on laser scanning[J]. Computer Science, 2016, 43(7):328-331. (in Chinese)
[16] Chen M, Zhang X, Liu X, et al. Estimation of sampling interval in terrestrial laser scanning data with neighboring analysis[C]//2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS. IEEE, 2021:8061-8064.
[17] Hackel T, Savinov N, Ladicky L, et al. Semantic3D.net:a new large-scale point cloud classification benchmark[J]. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2017, IV-1/W1, 91-98.
[18] Dong Z, Liang F, Yang B, et al. Registration of large-scale terrestrial laser scanner point clouds:a review and benchmark[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 163:327-342.
[19] 段月辉. 徕卡P50三维激光扫描仪在建筑地形图测绘中的应用[J]. 测绘通报, 2020(6):158-162. Duan Y H. Application of Leica ScanStation P503D laser scanner in building topographic mapping[J]. Bulletin of Surveying and Mapping, 2020(6):158-162. (in Chinese)
[20] Zhang W, Qi J, Wan P, et al. An easy-to-use airborne LiDAR data filtering method based on cloth simulation[J]. Remote sensing, 2016, 8(6):501.
