## 3 Results  
### Experiment Setup  
The proposed algorithm was verified using data recorded in an industrial hall with mineral resource storage. The dataset allowed testing the solution in a realistic scenario of the actual industrial site including a moving belt conveyor. The data were collected previously for the purpose of developing the idler malfunction detection presented in [4; 20]. The authors granted permission for the use of the recorded 3D LIDAR data in this study. The experiment comprised two runs, designated as Path A and Path B. The scheme of the experiment location is depicted in Figure 3. Illustrative images of the robot path and the conveyors with marked idler supports to be detected are presented in Figure 4.  
Path A was passing between two conveyors of a 0.7 m height. Path B was traversed in the opposite direction from Path A, with the same conveyor to the left as in the Path A and no conveyor to the right. Each path was about 150 m long. The mobile platform used for data collection is shown in Figure 5. The platform has a skid-steered kinematic system, i.e., four independently driven wheels on fixed axles. This type of mobile platform is characterized by the slippage of the wheels at turns; therefore, the localization of the robot with odometry, that is common for differential drive or car-like kinematic structures, is subject to significant error and therefore is unreliable without  
Figure 4: Images of the experiment location. (**a**) Path A. (**b**) Path B. Green rectangles mark the idlers’ supports to be detected. Figure 3: A scheme of the experiment location with marked robot path segments A and B.  
additional sensors like inertial measurement units (IMUs) or global localization modules like GPS. The experimental platform was not equipped with any of these sensors. This further supports the need of reliable localization using the available data. The platform carried a measurement module containing several sensors. That included an RGB and depth camera, a thermal imaging camera and 2D and 3D LIDAR sensors. However, for the needs of this study, only the data from the 3D LIDAR sensor were used. The 3D LIDAR sensor used in the experiment was Velodyne Puck. The sensor belongs to a popular type of spinning LIDAR sensors using the time-of-flight (ToF) method for the measurement of distance. The sensor uses a spinning array of 16 pairs of infrared (IR) lasers and detectors. Each pair measures distance within a full angle on a single cone's surface, with measurement surfaces vertically separated by an angular distance of 2\({}^{\circ}\). As a result, the field of view of the sensor is 30\({}^{\circ}\) vertically (from \(-15^{\circ}\) to 15\({}^{\circ}\)) and 360\({}^{\circ}\) horizontally with angular resolutions 2\({}^{\circ}\) and 0.1-0.4\({}^{\circ}\)(depending on settings), respectively, and a measurement range up to 100 m. The sensor returns measurements of up to 300,000 points per second in the Single Return Mode that was used in the experiment. The transformations of measured distances to the point cloud coordinates were performed by the ROS driver of the LIDAR sensor. The sensor was mounted at a height of 1.45 m from the ground, above the conveyor belts, tilted downward at approximately 26\({}^{\circ}\). The research on the thermal stability of the sensor [46] confirmed that the error of distance measurement in the range of 1.5 m is below 2 cm. The dataset used in the tests was collected during a single traverse by the robot. The processing relies solely on the LIDAR data. The collected set contains Velodyne scans acquired every 100 ms. The traverse of Path A contains 150 s of recording and Path B 94 s.  
To provide the ground truth for evaluating the algorithm, the supports were labeled by a human operator. During the process, the point cloud from each scan was inspected using 3D visualization. For each observed support, the operator marked a set of points perceived as belonging to that support. Then, in postprocessing, the mean points and bounding boxes for each support were calculated.