## 4 Discussion  
The results demonstrate that the proposed workflow based on density segmentation allows detection of the idler frames by a mobile robot in real time. In the experiments, two main aspects were considered: the accuracy of the method and the scan processing time. The experiments have indicated the influence of the number of LIDAR planes crossing the frame on the chances of the detection and of the occlusion by other objects, particularly the belts of the conveyors. It was observed that, when the number of LIDAR channels intersecting the frame was four or less, the accuracy of detection was relatively low, with the number of missed frames exceeding 25% and 35% depending on the test path. Increasing from five active channels, the accuracy improves, with the highest change visible for six channels. However, a higher number of active channels is associated with a smaller area of detection. The performed tests allowed finding a compromise between the accuracy of the detection and its range as the area with six active channels. This finding may be used practically in the design of inspection robots, providing guidelines to the selection of the sensors and their placement on the robot to optimize the area of detection. The performed tests also provided an evaluation of the influence of each stage of the workflow on the processing time. The major contribution to the total time comes from the cropping and alignment phase. This is caused mainly by the RANSAC algorithm, which has a complexity \(O(kN)\) where \(k\) is a number of iterations and \(N\) is the number of points. To alleviate that, the RANSAC algorithm is preceded by clipping the region of interest, reducing the number of points by almost half. The second in time share is the density segmentation phase, contributing about 30% to the total processing time, with the complexity linearly dependent on the number of points and the number of cells in the 2D histograms. These observations indicate that the highest potential for a further reduction in processing time is in the method of ground detection and removal. Future works should focus on improving this step. The approach proposed in this paper may be an alternative to other methods of object detection in the industrial environment known from the literature. While we are not aware of available benchmark scenarios that might be used for direct comparison, we may draw expectations from similar research. The most popular alternative for object detection uses computer vision and includes methods based on machine learning such \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Process Step** & **Min [ms]** & **Max [ms]** & **Mean [ms]** & **Std Dev [ms]** \\ \hline Cropping and alignment (CA) & 3.776 & 56.781 & 13.395 & 8.787 \\ Density segmentation (DS) & 0.197 & 27.711 & 6.798 & 3.559 \\ Support clustering (SCt) & 0.0 & 7.585 & 1.107 & 0.873 \\ Support classification (SCs) & 0.0 & 0.620 & 0.018 & 0.022 \\ Position estimation (PE) & 0.0 & 0.098 & 0.015 & 0.007 \\ Whole process (Total) & 6.258 & 74.940 & 21.334 & 10.658 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The durations at each stage of the point cloud processing along Path B.  
as YOLO (You Only Look Once) and traditional algorithms such as edge detection. An advantage of these method is the availability of the sensors and rapid development of methods, especially machine learning based. The application of YOLO and AFC (adaptive frame control) to general detection in [47] has shown that with a similar processing unit the method can achieve a 30 fps processing frequency; however, the overall latency from image acquisition to detection result exceeded 170 ms even for the best cases for images of resolution 416 \(\times\) 416. A similar latency was obtained for a process that combined edge detection and convolutional neural networks (CNNs) applied to the detection of conveyor belt deviation in [17]. Additionally, vision-based systems are more vulnerable to insufficient and varying lighting.