[3] [3] Zhang R Z, Yang B S, Xiao W, et al. Automatic Extraction of High-Voltage Power Transmission Objects from UAV Lidar Point Clouds[J]. Remote Sensing, 2019, 11(22): 2600.

[4] [4] Wen C C, Yang L N, Li X, et al. Directionally constrained fully convolutional neural network for airborne LiDAR point cloud classification[J]. ISPRS journal of photogrammetry and remote sensing, 2020, 162(1): 50-62.

[5] [5] Guan H Y, Yu Y T, Li J H, et al. Extraction of power-transmission lines from vehicle-borne lidar data[J]. International Journal of Remote Sensing, 2016, 37(1): 229-247.

[8] [8] Qi C R, Yi L, Su H, et al. Pointnet++: Deep hierarchical feature learning on point sets in a metric space[J]. Advances in neural information processing systems, 2017, 30(1): arXiv. 1706.02413.

[9] [9] Zhang J, Singh S. LOAM: Lidar odometry and mapping in real-time[C]//Robotics: Science and systems. Berkeley, California, USA: MIT Press, 2014: 1-9.

[10] [10] Shan T X, Englot B. Lego-loam: Lightweight and ground-optimized lidar odometry and mapping on variable terrain[C]//2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Madrid, Spain: IEEE Press, 2018: 4758-4765.

[11] [11] Shan T X, Englot B, Meyers D, et al. Lio-sam: Tightly-coupled lidar inertial odometry via smoothing and mapping[C]//2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Las Vegas, NV, USA: IEEE Press, 2020: 5135-5142.

[13] [13] Xu W, Cai Y X, He D J, et al. Fast-lio2: Fast direct lidar-inertial odometry[J]. IEEE Transactions on Robotics, 2022, 38(4): 2053-2073.

[14] [14] Roumeliotis S I, Sukhatme G S, Bekey G A. Circumventing dynamic modeling: evaluation of the error-state Kalman filter applied to mobile robot localization[C]//IEEE International Conference on Robotics & Automation, 2002: 1656-1663.

[15] [15] Hu Q Y, Yang B, Xie L H, et al. Randla-Net: Efficient semantic segmentation of large-scale point clouds[J]. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2020: 11105-11114.

[16] [16] Groh F, Wieschollek P, Lensch H P. Flex-Convolution: Million-scale point-cloud learning beyond grid-worlds[C]//Asian Conference on Computer Vision: Springer, 2018: 105-122.

[17] [17] Qi C R, Su H, Mo K C, et al. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation[J]. CoRR, 2016.

[18] [18] Thomas H, Qi C R, Deschaud J, et al. KPConv: Flexible and Deformable Convolution for Point Clouds[J]. CoRR, 2019.