## 2 Research Status of Key Technology for Environmental Perception of Intelligent Excavators
### Research Status of Multi-Sensor Information Fusion Technology
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Literature** & **Keywords** & **Major Contribution** & **Time** \\ \hline
[17] & Cameras and LiDAR & Cameras and LiDAR are used for environmental perception, and the ICP algorithm is used to fuse images and point cloud information. & 2013 \\ \hline
[60] & LiDAR and binocular camera & The local terrain reconstruction system of intelligent excavators is developed by using LiDAR and binocular cameras, which effectively improves the environmental perception ability of intelligent excavators. & 2017 \\ \hline
[61,62] & projection & The point cloud data is projected onto the image for data fusion. & 2018 \\ \hline
[63] & Gaussian interpolation & After projection fusion, the geometric transformation relationship between different sensors is calculated, and the image pixels lacking depth information are compensated by the Gaussian interpolation algorithm. & 2018 \\ \hline
[64] & Gaussian process regression interpolation & The color information of the image is assigned to the point cloud by projection, and the resolution of the LiDAR data is matched by Gaussian process regression interpolation. & 2019 \\ \hline
[65] & Local distance modeling, 3D depth map reconstruction & In the local scale modeling stage, the three-dimensional point cloud is interpolated using Gaussian process regression, and in the reconstruction stage, the image and interpolation points are fused to build a three-dimensional depth map, and the merged data is optimized based on Markov random fields. & 2016 \\ \hline
[66] & Full-dimensional color laser ranging system & An omnidirectional 3D color laser ranging system is developed. Based on the improved rectangular hole checkerboard calibration method, the 2D image and point cloud are fused into a 3D color point cloud. & 2019 \\ \hline
[67] & Registration, plane fitting & The initial feature is extracted from the image according to the gradient direction, and the conversion from 2D image to 3D point cloud is realized on the basis of registration, and then the point cloud data around the feature line is fitted in the plane. & 2016 \\ \hline
[68] & Complementary advantages & Taking full advantage of the complementary advantages of LiDAR data and optical imaging, different architectural features are extracted from the two data sources and fused to form the final complete architectural boundary. & 2013 \\ \hline
[69] & Feature point matching & Combining the depth information of LiDAR and the color texture information of an image, the matching relationship of feature points is constructed to solve the location problem of outdoor robots in complex environments. & 2019 \\ \hline
[70] & Timely positioning and accurate composition & The visual information, LiDAR information, and odometer information are integrated to solve the problem of timely positioning and accurate composition of mobile robots in an unknown environment. & 2019 \\ \hline
[71] & Fusion, clustering & The laser radar depth information is mapped to two-dimensional camera images for fusion, and real-time pedestrian detection is realized by cluster analysis and support vector machine classification. & 2019 \\ \hline
[72] & Vehicle real-time detection & Hough transform and Chebyshev’s theorem is used to remove the non-target points in the radar data channel, and the significant image of the vehicle is generated in the image data channel, and then the vehicle position is located in real-time by segmentation method. & 2015 \\ \hline
[73] & Loop, high-precision & A multi-sensor data fusion scheme of a tightly coupled LiDAR-camera-IMU system is proposed, and the loop optimization of single-vehicle and multi-vehicle is realized. & 2024 \\ \hline
[74] & Information fusion; Tracking method; Particle filter algorithm & Multiple sensors are used to collect the trajectory information of the robot at the same time, and the information is fused accordingly. Then, the particle filter algorithm is introduced to realize the trajectory tracking of the robot. Finally, the comparison experiment of the trajectory tracking of the robot is carried out with other methods. & 2024 \\ \hline \hline \end{tabular}
\end{table}