## 2 Research Status of Key Technology for Environmental Perception of Intelligent Excavators
### Research Status of Segmentation and Recognition Technology of 3D Operation Scenes
[89] segmented point cloud data after the removal of ground points through raster processing, and set the grid and its neighborhood as local point clouds. Then, they extracted the reflective features of the local point cloud and used Hof forest to segment and identify the targets such as trees and vehicles in the urban environment. [PERSON] et al. [90] conducted multi-scale sampling of point cloud data and classified the sampling points into different levels of point cloud clusters. Then, vehicles, pedestrians, buildings, and other targets in the urban scene were identified based on the Bayesian probabilistic model and AdaBoost classifier. Traditional machine learning algorithms rely heavily on artificial feature extraction. It is difficult to extract complex features accurately, resulting in lower segmentation recognition accuracy. Deep learning algorithms can be divided into two categories according to the different processing methods of the point cloud. One is to regularize the disordered point cloud first, such as projecting a 3D point cloud onto a 2D plane [91; 92; 93; 94; 95]. Alternatively, the disordered point cloud is rasterized and voxelized [96; 97; 98; 99], and then processed by convolutional neural networks (CNNS). This kind of algorithm is easier to understand, and can effectively combine the successful experience of a two-dimensional image semantic segmentation algorithm to easily transplant the image semantic segmentation algorithm to three-dimensional point cloud semantic segmentation. However, point cloud preprocessing will lose certain point cloud information, which will have adverse effects on point cloud feature extraction, especially point cloud processing in fine-grained complex scenes [100]. The other type directly processes the original point cloud data and does not transform the point cloud data before input to the network to save the point cloud information to the maximum extent. This type of algorithm has strong anti-noise ability and a high intelligence degree and can obtain relatively robust and accurate segmentation and recognition results for point clouds with different quality and density distributions. It is more in line with the need for intelligent autonomous operation of excavators. PointNet, proposed by [PERSON] et al. [101], was the first network to directly input original point cloud data for segmentation and recognition. This network solved problems such as point cloud disorder and geometric rotation invariance, and the experimental results showed that breakthrough progress had been made in both point cloud classification and semantic segmentation. However, the algorithm does not make use of local feature information effectively, and the perception ability of local spatial features is poor. To solve the problems existing in PointNet, [PERSON] et al. [102] proposed PointNet++ and constructed a hierarchical neural network structure. At each layer, point clouds were first sampled and divided into regions, and then the basic PointNet network was used to extract local features in each region. These local features are then further sampled and zoned as part of the input to extract higher-level features. Pointnet ++ improves the ability to extract local features of the point cloud and effectively improves the segmentation and recognition effect of point cloud targets such as walls and roofs. [PERSON] et al. [103] proposed PointSIFT, a three-dimensional point cloud segmentation model based on scale-invariant feature transformation. In this model, multiple main direction information points are encoded by directional coding units, and multiple coding units are stacked to obtain multi-scale features. [PERSON] et al. [104] proposed a PointWeb 3D point cloud segmentation model, which connected each point in the local neighborhood to better extract local point cloud features, and proposed an adaptive feature adjustment module to enhance point-by-point feature expression capability. [PERSON] et al.