# Retro-Reflective-Marker-Aided Target Pose Estimation in a Safety-Critical Environment
## 3 Results
### Real Data
#### 3.2.8 Discussion  
Through the study of the overall performance of the system, we conclude that both the monocular and stereoscopic approaches satisfy the requirements of the application for the studied range of positions, as all data points are within the \(\pm\)3 mm tolerance (Figure 16). Furthermore, the results are considered to be quite precise, as the errors are distributed within a circle of approximately 1 mm radius around the mean value. There are no significant performance differences between the monocular and stereoscopic approaches. This indicates that the system can be simplified to its monocular version without significant loss of accuracy or precision. This is a clear improvement over methods based on 3D reconstruction from multiple cameras, since the physical space on the manipulator is limited. When using the reference method for marker detection (checkerboard target), we observe a similar trend in the results (Figure 17). However, precision is lower, with errors  
Figure 23: Example of a cropped image taken at the aligned position. The yellow cross represents the center of the marker and the red cross the center of the image. A combined overall performance error can be calculated, in pixels, as the difference between the two points. Figure 22: Example image taken at the aligned position. If the estimate is correct, the center of the reference marker should correspond to the center of the image. distributed in a circle of around 1.5 mm radius around the mean value, which is likely related with the increased distance between the camera and the target. In all experiments, regardless of the evaluation metric, we observe an offset in the estimated errors. When relating the local coordinate systems of the targets with the physical setup, it becomes clear that the offset has a strong directional component that coincides with the gravity vector. Furthermore, the remaining directional components of the offset tend to follow the direction in which the end effector extends from the base. The offset can be traced back to an error in the positions reported by the control system of the robot, and it is higher the further the arm extends from the robot base. This data leads us to conclude that the offset is related to a deflection of the robot base under the manipulator's weight. This is a known problem in robotics, often tackled through non-kinematic calibration of the manipulator. Non-kinematic calibration of robot manipulators is of particular interest to the ITER application due to the fact that the high payloads can cause considerable deflections of the robot base [53]. However, the investigation of such methods falls out of the scope of this work, and in our results, no methods have been applied to compensate for this deviation. Therefore, in the upcoming discussion on how several characteristics of the initial pose affect the quality of our estimate, we consider better estimates those that are nearest the mean error value. In this setting, it is clear from Figure 18 that the overall system provides better estimates for less slanted initial camera positions. This is likely influenced by the characteristics of the developed marker detection algorithm, which assumes that the centroid of the detected ellipses in the images corresponds to the center of the circular markers. This approximation is better when the camera plane and the object plane are parallel. We also note that the stereoscopic estimation seems to have a more advantageous performance for higher angles, reducing the spread of the results and increasing the precision of our estimation. In Figure 19, we see that a higher distance from the central position is similarly associated with worst estimates, although the difference is not as obvious as in the previous case. As Figures 18 and 19 indicate, images from initial poses with lower angle and smaller distance generally lead to consistent estimates closest to the mean error, i.e., have the best precision. In Figure 20, we see a slightly different pattern.