## 5 Results and Analysis  
In order to validate the performance of the proposed system, a model of the cargo box of a common heavy-duty dump truck was developed, followed by outdoor experiments. The excavator sensor was installed on the upper section of the driver's seat of the excavator as shown in Figure 18, and the loading procedure was repeated 20 times as the sensor sensed the cargo box while the excavator was moving. The data, as shown in Figure 19, were obtained each time, and the distance values were derived from the stereo sensor. Figure 16: Example of the 3D points of a 3D model projected onto a 2D image of the camera. Figure 17: A method of selecting the correspondence point of the truck cargo box from the corners. Figure 18: Three-dimensional location estimation experiment for the cargo box of dump truck: (**a**) an experimental model built for experimentation; (**b**) an experiment example using an excavator. The sensor is placed on the top of the excavator. Figure 20 illustrates the detectability of the truck cargo box door based on distance measurements from the LiDAR sensor. When the distance data obtained by the LiDAR sensor and the stereo camera were visualized, only those points that were estimated to represent the cargo box door were visible. Notably, the widely used dump truck cargo boxes of Volvo and Scania were sensed, excluding the experiment model, and an experiment for detecting the cargo box door was conducted [24; 25]. Using the plane data obtained from the LiDAR sensor, the 3D point cloud obtained from the camera was projected. The cargo box doors of two regular trucks are also detected  
Figure 21 shows the experimental results of 3D location estimation using the experimental model. The initial results are indicated in blue, and the more precise results using a refined transformation matrix are expressed in yellow. When the initial position was estimated only using plane data, numerous differences were found in the longitudinal axis of the cargo box. The 3D position estimation using the values obtained by the refined transformation matrix showed that the longitudinal axes of the cargo box and the hexahedral model were similar, indicating that the 3D location estimation was successful. Estimation of cargo space volume can increase work efficiency [26; 27]. The 3D location of the estimated cargo box enabled the provision of data of the loading space of the cargo box. Figure 22 illustrates the visualized results of normalized data based on box height. The load can be placed in the cargo box, excluding the part in red. Figure 19: Sensing data obtained from the sensor: (**a**) RGB Image; (**b**) Depth Map; (**c**) LIDAR data. Figure 20: The result of detecting the doors of two commercial dump trucks with LiDAR data. The green point cloud is the point cloud obtained from the stereo camera projected onto the dump truck door plane. In this study, the load was loaded five times to the cargo box interior using the excavator, as shown in Figure 23, and volume estimations were made using the image data obtained from the camera. The cargo box interior was detected using the image data when the 3D hexahedral model was projected onto the image, as shown in Figure 24. As such, the outcome of the projected 3D points was expressed in the shape of a trapezoid (near-far effect), for which the distance per pixel was considered to estimate an accurate value. The distance per pixel, \(p_{d}\), is calculated using Equation (10):  
\[p_{d}=real\ distance(mm)/\ pixel. \tag{10}\]  
Figure 21: The result of precisely finding the three-dimensional position of the truck cargo box using the proposed method. Blue is the initial 3D position of the cargo box, and yellow is the result of a more precise 3D position. The side corners of the cargo box are more precisely matched. Figure 22: Visualization of the inside of the loader analysis. The excavator can load loads in areas other than the red area.