Efficient Filtering and Smoothing Algorithm For Train Key Components Based on Scattered Point Clouds

Fig. 4. Comparison of train wheel pair point clouds before and after filtering. (a) Standard point cloud; (b) after applying Gaussian noise; (c) bilateral filtering; (d) guided filtering; (e) adaptive weighted guided filtering

Download full size

Fig. 5. Comparison of train bogie point clouds before and after filtering. (a) Standard point cloud; (b) after applying Gaussian noise; (c) bilateral filtering; (d) guided filtering; (e) adaptive weighted guided filtering

Download full size

Fig. 6. Comparison of train component 1. (a) Original point cloud; (b) bilateral filtering; (c) guided filtering; (d) adaptive weighted guided filtering

Download full size

Fig. 7. Comparison of train component 2. (a) Original point cloud; (b) bilateral filtering; (c) guided filtering; (d) adaptive weighted guided filtering

Download full size

Fig. 8. Comparison of train component 3. (a) Original point cloud; (b) bilateral filtering; (c) guided filtering; (d) adaptive weighted guided filtering

Download full size

Fig. 9. Comparison of train component 4. (a) Original point cloud; (b) bilateral filtering; (c) guided filtering; (d) adaptive weighted guided filtering

Download full size

Fig. 10. Comparison of train component 5. (a) Original point cloud; (b) bilateral filtering; (c) guided filtering; (d) adaptive weighted guided filtering

Download full size

Fig. 11. Comparison of train component 6. (a) Original point cloud; (b) bilateral filtering; (c) guided filtering; (d) adaptive weighted guided filtering

Download full size

Table 1. Train wheel pair point cloud filtering results
View table
Table 1. Train wheel pair point cloud filtering results
Train wheel pair Time /ms $D_{m a x}$ $D_{m e a n}$ $D_{s t d}$
Gaussian noise 92.69 16.70 10.50
BF 1419.20 59.28 10.75 6.78
GF 560.48 56.45 10.62 6.28
AWGF 715.10 56.79 10.39 6.14

Table 2. Train bogie point cloud filtering results
View table
Table 2. Train bogie point cloud filtering results
Train bogie Time /ms $D_{m a x}$ $D_{m e a n}$ $D_{s t d}$
Gaussian noise 91.10 14.70 10.09
BF 35081.10 84.33 9.70 6.28
GF 15886.60 56.11 9.62 5.90
AWGF 16072.50 55.10 9.29 5.74

Table 3. Number of point clouds of train components
View table
Table 3. Number of point clouds of train components
Train key component Number of points
1 218452
2 332895
3 523009
4 843918
5 1029027
6 1834880

Table 4. Comparison of filtering time for different number of point cloud components
View table
Table 4. Comparison of filtering time for different number of point cloud components
Train component 1 2 3 4 5 6
BF 10086.2 15451.8 24106.4 32991.5 43347.6 78469.9
GF 4274.4 6579.5 10225.4 16610.5 19983.2 36216.9
AWGF 4376.1 6803.4 10667.7 17129.0 20345.7 37077.5

Tools

Get Citation

Copy Citation Text

Ni Zeng, Jinlong Li, Xiaorong Gao, Yu Zhang, Lin Luo. Efficient Filtering and Smoothing Algorithm For Train Key Components Based on Scattered Point Clouds[J]. Laser & Optoelectronics Progress, 2023, 60(14): 1410011

Download Citation

EndNote(RIS)BibTex Plain Text

Set citation alerts for article

Save article for my favorites