## 2 Research Status of Key Technology for Environmental Perception of Intelligent Excavators
### Research Status of LiDAR Dust Noise Reduction Technology
[PERSON] et al. [54] used the improved bilateral filtering algorithm to remove the point cloud noise. First, the point was judged to be a feature point or a non-feature point. Then, the bilateral filtering factors of feature points and non-feature points were calculated according to the point clouds in different ranges. In this way, bilateral filtering point cloud denoising based on feature selection is achieved, which effectively maintains the features of the scanned object while removing the noise. [PERSON] et al. [55] proposed a 3D point cloud denoising algorithm based on feature information classification. The principal component analysis method and quadric surface fitting method were integrated to estimate the geometric information of 3D point clouds, and then point clouds were divided into regions with rich feature information and regions with less feature information according to the average curvature of point clouds. Adaptive bilateral filtering algorithm and neighborhood distance average filtering algorithm were used to reduce noise in different regions. Table 3 summarizes the methods proposed in the literature [9; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59]. All the above methods adopt noise reduction filtering to filter the noise generated under the influence of harsh environments. However, the data generated after noise filtering were not effectively repaired. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Literature** & **Keywords** & **Major Contribution** & **Time** \\ \hline
[9] & Final echo technique &
\begin{tabular}{c} By extracting the edge of the final pulse, the target and dust signal are \\ distinguished, which effectively improves the loading target recognition \\ effect under dust conditions. \\ \end{tabular} & 1999 \\ \hline
[47] & Dust influence rule &
\begin{tabular}{c} The dust influence rule of common LiDAR under different measuring \\ distances, different dust concentrations, and different reflectance is \\ summarized. \\ \end{tabular} & 2017 \\ \hline
[48] & Registration fusion &
\begin{tabular}{c} The influence of dust on environmental perception accuracy is reduced \\ through the fusion of two-dimensional color images and three-dimensional \\ point cloud registration. \\ \end{tabular} & 2014 \\ \hline
[49] & Background noise &
\begin{tabular}{c} The noise level equation is derived from the LiDAR equation, and the \\ background noise equation is verified by using the observed LiDAR data \\ and the simulated signal. \\ \end{tabular} & 2013 \\ \hline
[50] & Differential optical path &
\begin{tabular}{c} A LiDAR denoising method based on differential optical path is proposed, \\ the mathematical model is derived and verified, and the signal-to-noise ratio \\ is simulated based on this method. \\ \end{tabular} & 2017 \\ \hline
[51] & Probabilistic model &
\begin{tabular}{c} A probabilistic model of the interaction between LiDAR and dust is \\ established, and the model is verified by simulation and experiment. \\ \end{tabular} & 2013 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Research status of LiDAR dust reduction technology.