LiDAR Point Cloud Recognition and Visualization with Deep Learning for Overhead Contact Inspection

. 2020 Nov 9;20(21):6387. doi: 10.3390/s20216387

Algorithm 1 Visualization algorithm

Input: files that store point cloud data

Output: visualization results

1:
Set the control button “select file” to realize the function of selecting the file path
2:
Set the control button “read file” to realize the function of reading the file from the above file path
3:
if the point cloud data are the raw data collected by the 2D SICK LiDAR sensor then
4:
convert the 2D point cloud data into 3D data
5:
obtain the 3D coordinate of the 3D data
6:
end if
7:
Process each row of 3D data
8:
Store all frames in the list A, and obtain Z frames of data
9:
Set the scale range of the X-axis and Y-axis, and display the coordinate axes
10:
forz = 1, Z do
11:
take out the coordinates of each frame of data
12:
assign the above coordinates to the display control $D_{1}$
13:
end for
14:
Achieve the function of the display control $D_{2}$
15:
Mark the display content of $D_{2}$ with different colors to display the different OC components
16:
Realize the functions of dragging, zooming in, and zooming out for the display controls $D_{1}$ and $D_{2}$