## 2 Research Status of Key Technology for Environmental Perception of Intelligent Excavators
### Research Status of Segmentation and Recognition Technology of 3D Operation Scenes  
The three-dimensional point cloud can accurately record the location and geometric information of the measured target; it has advantages that two-dimensional images cannot match. This important information is the basis for intelligent excavators to carry out tasks such as path planning and trajectory planning, so the intelligent excavator operation scene perception data is mainly the three-dimensional point cloud. The three-dimensional operation scene of intelligent excavators is complex and dynamic, and the main targets to be divided and identified include piles, vehicles, personnel, trees, buildings, ground, etc., which is extensive and diverse. Aiming at the segmentation and recognition of the intelligent excavator's three-dimensional operation scene, [PERSON] et al. [77] proposed a multi-scale edge detection algorithm by constructing a quadratic B-spline wavelet. Based on this algorithm, the edge features of the loading vehicle of an intelligent excavator were detected and recognized. [PERSON] et al. [78] proposed a method based on point cloud clustering feature histogram for the identification of intelligent excavator operation targets. This method can simultaneously identify multiple targets such as intelligent excavator loading vehicles and material piles and has good stability. Aiming at intelligent mining excavators under the influence of dust, [PERSON] et al. [79] proposed a target segmentation and identification method based on maximum evidence, which improved the accuracy and robustness of segmentation and identification. This method achieves the segmentation and recognition of operational targets such as buckets and piles. In addition, aiming at the anti-collision problem of mining excavator buckets and mining cards, they divided and identified the edges of buckets and mining cards [80]. At present, there are few research results on the segmentation and recognition technology for the three-dimensional operation of intelligent excavators, but relevant research in other fields can have certain reference significance for the research and development of intelligent excavators. This research can be divided into traditional machine learning algorithms and deep learning algorithms. The traditional machine learning algorithm extracts sample features and uses the classifier to complete the segmentation and recognition of 3D job scenes. The use of Markov random fields [81], random forests [82], support vector machines [83,84] and other models has improved the recognition speed and accuracy. [PERSON] et al. [85] comprehensively adopted a random forest classifier and conditional random field and used reflection intensity, curvature and other features to identify buildings in three-dimensional point cloud scenes. [PERSON] et al. [86] used the graph-cutting method to segment the point cloud scene, and then extracted features such as target location and nearest distance to the roadside, and applied a support vector machine to classify and recognize objects such as vehicles and street lights. [PERSON] et al. [87] extracted spectral, geometric, and spatial features of 3D point cloud data, and then used the AdaBoost classifier to extract trees from urban scenes. [PERSON] et al. [88] segmented a three-dimensional point cloud using the scanning line algorithm and extract the height of the segmented surface, normal vector variance, and other features. They also used a support vector machine to classify and  
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\begin{tabular}{c} An object association fusion algorithm among distributed sensors based on \\ the covariance of object state estimation error is proposed. Independent \\ sequential object association algorithm and weighted fusion algorithm are \\ used to associate and fuse each sensor object after a coordinate \\ transformation, which effectively improves the reliability and accuracy of \\ fusion system. recognize targets such as pedestrians and trees. [PERSON] et al.