# MViDO: A High Performance Monocular Vision-Based System for Docking A Hovering AUV
## 5 Experimental Results: Pose Estimator and Tracking System
### Results
#### 5.2.2 Tracking System  
For the parametrization of the filters, the choice of the number of particles was related to the computational resources, namely with the processing time of each frame. It was necessary to establish a compromise between the adjustment of the number of particles and the processing time. For the particle number adjustment, we chose to analyze the effect of an increase in the number of particles (per marker) at moments in which the AUV was in a hovering situation after dynamic moments. Each time we adjust the number of particles, the threads time of the algorithm was measured as shown in the Figure 31. This time will give us an idea of the rate at which we provide the estimation data to the control. In addition to the number of particles, it was also analyzed the parametrization of the uncertainties of update (observations) and prediction (a static model) of the filter. For the update uncertainties, the values were adjusted according to the characterization of the pose estimator. For the adjustment of the prediction uncertainties was considered the typical velocity of the AUV Mares, the maximal diving velocity of the AUV Mares and the characteristics of the camera. With the parameterized filter, we compared the results by using the filter with the results without the use of the filter. Figure 32 represents the descent of the AUV towards the target. In the graph of Figure 32 it is possible to see the performance of the estimations algorithm with and without the use of the filter. To generate the graph of the Figure 32, 360 frames were sampled. The outlier occurs from the frame 44 to frame 108. The temporary occlusions occur from the frame 124 to frame 284. There were several partial occlusions, sometimes partial occlusion of a marker, sometimes partial occlusion of two markers. Figure 31: Threads time: in the parametrization of the filter, the adjustment of the number of particles is made taking into account the processing time of each frame. It is intended to observe the target at a minimum rate of 10 frames per second (less than 100 ms). This is due to the fact that the control operates at 20 Hz and it is not desired that there is more than one estimation between two consecutive frames. That is, it is desired that the algorithm has a processing time of less than 10 ms. The processing time of the developed filter for 1000 particles occupies 13 ms of the total processing time. In addition to the detector/estimator processing time (83 ms) it is found that the goal of a total processing time of less than 100 ms has been achieved. The equipment used was the developed vision module (Camera Bowtech + Raspberry Pi) whose characteristics are described at the beginning of this chapter. Figure 30: Histogram of \(R_{z}\) for 90\({}^{\circ}\) of rotation. The curve in blue is the curve estimated by R as the Gaussian curve that is closest to the obtained data. The Figure 33 is a detail of the graph of the Figure 32, from the frame 44 to frame 108, and allows us to observe how the algorithm behaves in the outlier zone. In the graph of the Figure 33 the resultant curve of the pose estimator is compared with the resulting curve of the estimator with the use of the filter, in a situation of outliers detection. The Figure 34 is a detail of the graph of the Figure 32, from the frame 124 to frame 284, and allows us to observe how the algorithm behaves in the partial occlusions zone. As can be seen in Figure 34, the use of the filter allows the continuity in obtaining estimates during situations of temporary and partial occlusions of the markers. However in areas where occlusions occur more frequently, the performance can be improved, and for this reason the constraints of the target geometry were added to the filter. The graph of the Figure 35 is a detail of the graph of the Figure 32, from the frame 124 to frame 284, where we can observe how the filter behaves in the temporary and partial occlusions zone when the geometric constraints were added to the filter.