[1] Li C Y, Peng C, Zhang Z Q et al. Positioning and map construction for agricultural robots integrating odometer information[J]. Transactions of the Chinese Society of Agricultural Engineering, 37, 16-23(2021).

[2] Shen Y, Xiao X H, Liu H et al. Real-time localization and mapping method for agricultural robot in orchards based on LiDAR/IMU tight-coupling[J]. Transactions of the Chinese Society for Agricultural Machinery, 54, 20-28, 48(2023).

[3] Zhou M, Liu S C, Xie L B et al. LiDAR static mapping method based on spatio-temporal constraints[J]. Acta Optica Sinica, 45, 0628005(2025).

[4] Song Y, Xu F Y, Yao Q et al. Navigation algorithm based on semantic segmentation in wheat fields using an RGB-D camera[J]. Information Processing in Agriculture, 10, 475-490(2023).

[5] Li Z K, Deng X L, Lan Y B et al. Fruit tree canopy segmentation from UAV orthophoto maps based on a lightweight improved U-Net[J]. Computers and Electronics in Agriculture, 217, 108538(2024).

[6] Zhou C, Shu Z, Shi L et al. Semantic segmentation for large-scale point clouds based on hybrid attention and dynamic fusion[J]. Pattern Recognition, 156, 110798(2024).

[7] Wu Y Q, Chen H X, Zhang Y. Review of 3D point cloud processing methods based on deep learning[J]. Chinese Journal of Lasers, 51, 0509001(2024).

[8] Charles R Q, Hao S, Mo K C et al. PointNet: deep learning on point sets for 3D classification and segmentation[C], 77-85(2017).

[9] Charles R Q, Yi L, Su H et al. Pointnet++: deep hierarchical feature learning on point sets in a metric space[EB/OL]. https://arxiv.org/abs/1706.02413

[10] Wang Y, Sun Y B, Liu Z W et al. Dynamic graph CNN for learning on point clouds[J]. ACM Transactions on Graphics, 38, 1-12(2019).

[11] Zhao H S, Jiang L, Fu C W et al. PointWeb: enhancing local neighborhood features for point cloud processing[C], 5560-5568(2019).

[12] Li Y Y, Bu R, Sun M C et al. PointCNN: convolution on X-transformed points[EB/OL]. https://arxiv.org/abs/1801.07791

[13] Xu J, Liu H, Shen Y et al. Point clouds classification and segmentation for nursery trees based on improved PointNet++ model[J]. Chinese Journal of Lasers, 51, 0810001(2024).

[14] Xu J, Liu H, Shen Y et al. Individual nursery trees classification and segmentation using a point cloud-based neural network with dense connection pattern[J]. Scientia Horticulturae, 328, 112945(2024).

[15] Liu H, Wang X L, Shen Y et al. Multi-objective classification method of nursery scene based on 3D laser point cloud[J]. Journal of Zhejiang University (Engineering Science), 57, 2430-2438(2023).

[16] Dong H X, An Y, Xie L R et al. Semantic segmentation of large-scale laser point cloud in mines based on local feature enhancement[J]. Chinese Journal of Lasers, 51, 1710002(2024).

[17] Hu Q Y, Yang B, Xie L H et al. RandLA-net: efficient semantic segmentation of large-scale point clouds[C], 11105-11114(2020).

[18] Chen Y, Xiong Y J, Zhang B H et al. 3D point cloud semantic segmentation toward large-scale unstructured agricultural scene classification[J]. Computers and Electronics in Agriculture, 190, 106445(2021).

[19] Wei H J, Xu E Y, Zhang J L et al. BushNet: Effective semantic segmentation of bush in large-scale point clouds[J]. Computers and Electronics in Agriculture, 193, 106653(2022).

[20] Vaswani A, Shazeer N, Parmar N et al. Attention is all you need[EB/OL]. https://arxiv.org/abs/1706.03762

[21] Su Y F, Liu W Q, Yuan Z M et al. DLA-Net: Learning dual local attention features for semantic segmentation of large-scale building facade point clouds[J]. Pattern Recognition, 123, 108372(2022).

[22] Zhao H S, Jiang L, Jia J Y et al. Point transformer[C], 16239-16248(2021).

[23] Guo M H, Cai J X, Liu Z N et al. PCT: point cloud transformer[J]. Computational Visual Media, 7, 187-199(2021).

[24] Wang X L, Girshick R, Gupta A et al. Non-local neural networks[C], 7794-7803(2018).

[25] Cao Y, Xu J R, Lin S et al. GCNet: non-local networks meet squeeze-excitation networks and beyond[C], 1971-1980(2019).

[26] Gao S H, Cheng M M, Zhao K et al. Res2Net: a new multi-scale backbone architecture[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43, 652-662(2021).