## 3 Results
### Single-Scan Point Cloud Processing  
This section presents the results of processing of a single scan. The stages are presented and commented on separately to allow the evaluation of the influence of each stage on the final results. The cropping and alignment stage transforms an acquired point cloud through the four processing steps. Figure 6 illustrates how the processed point cloud changes through the elimination of points at each step. Figure 6a presents the points obtained from the scanner. The red rectangle indicates the actual area to be considered, where the robot path and the conveyors are located. It may be noticed that the long range of the scanner provides a huge number of points which are irrelevant to the idler frame detection. There may even be observed distant walls and the ceiling of the hall. For that reason, in the first step, the distant points are removed. The threshold distance for removal was 5 m. Figure 6b shows the remaining points. The red markings indicate the location of the idlers that are to be found. In the next step, the RANSAC algorithm is used to detect the ground plane. The distance threshold \(d_{gmin}\) was set to 0.05 m. The result of ground detection are the points in red in Figure 6c. The red points, which are within \(d_{gmin}\) of the ground plane, are removed. After removing the ground points and alignment of the ground plane with the horizontal plane, the processed point cloud receives the form shown in Figure 6d, with colors indicating the Z coordinate. The region of interest was defined as \(l=5\) m long, \(h=1.5\) m high, and \(w=4\) m wide. One of the main purposes of this stage is a reduction in the number of points to process. To evaluate how many points are eliminated in each step of this stage, a scan-by-scan analysis was performed for both paths. The results for Path A are presented in Table 1 and for Path B in Table 2.  
Figure 6: Transformation of a point cloud in preprocessing stage. (**a**) Original image from the LIDAR sensor. The red rectangle indicates the area with conveyors. (**b**) Point cloud with distant points clipped. The boxes show the location of idler supports. (**c**) The results of the RANSAC algorithm—the ground points marked in red. (**d**) Aligned point cloud with ground removal. The tables present the minimum and maximum value encountered on each path, the average, and standard deviation. The consecutive rows present the initial number of points in the data from the LIDAR and after each step filtering the points. It may be noted that in the minimum, maximum, and average values there is not much difference between the two paths. However, the standard deviation is approximately doubled for Path B. This corresponds to the more variable environment to the right of the robot along Path B (cf. Figure 4b). Figure 7 present the 2D histograms of points for the top and front projection for the cell size \(a=0.1\) m. The red marking on the 2D histograms indicates the cells including the idler frames and conveyor belts. In the top projection, the idler cells are clearly visible as local maxima of the 2D histogram along the X axis. The parameters used to classify a histogram cell as containing an idler candidate were \(b_{r}=3\) and \(b_{i}=30\). However, in the front view, the maximum is occupied by the elongated objects, i.e., belts and barriers. To obtain the cells representing elongated objects, a threshold of \(b_{th}=30\) was applied. Figure 8 presents the steps of density classification to obtain idler frame candidates. The points selected from the top projection segmentation are highlighted in Figure 8a. Figure 8b displays the elongated objects obtained from the front projection. Finally, the result of the set difference operation between the points from the two projections is presented in Figure 8c.