# MViDO: A High Performance Monocular Vision-Based System for Docking A Hovering AUV
## 7 Discussion  
### Pose Estimator  
A target was constructed whose physical characteristics maximize its observability. The three color markers are spherical in order to be observable from different perspectives. We chose to use different colors for each of the markers in order to avoid ambiguities in the relative pose estimation. The solution is based on passive visual markers, occasionally illuminated outside only in certain circumstances of visibility. The choice of dimensions for the target was related to three factors: the maximum operating distance \(WD\), the field of view of the lens and the constructive characteristics of the dock. For the relative pose estimation, it was intended to explore as much as possible a minimum system, based only on visual information and with the minimum calculations processing time. The results obtained were more than satisfactory considering that, in the worst case, the accuracy for position estimation was around 7 cm for a working distance of 2.5 m, with the target rotated of 60\({}^{\circ}\) around the \(x\) axis. That working distance is higher than the intended maximum working distance of 2 m. The maximum average error in target rotation was less than four degrees for a critical situation where the target was rotated 60 degrees on the \(y\) axis. From the experimental characterization of the system, we can also conclude that there is an increase of the error as the target moves away from the camera, that is, as the working distance \(WD\) increases. This increase in the value of the error is due to four reasons:  
* a misalignment of the camera related to the rail: the nonexistence of a mechanical solution that guarantees the correct alignment between the center of the camera to the tube rail. This misalignment has influence especially on the Y axis, which means a yaw rotation of the camera related to the rail,
* the increasing offset along the z-axis is related to the non-calibration of the value of the focal length of the cameras lens,
* small variations in markers illumination that affect the accuracy in detecting the center of mass of each marker in the image,
* the more the target is away from the camera, any small variation in the detection of the blobs in the image will imply a greater error in the pose estimation. The pinhole model shows that as the target moves away (any small variation on the sensor side (IMAGE) implies a greater sensitivity and a greater error on the SCENE side).  
Despite these systematic observed errors that can be easily corrected, the results obtained for the markers detector and pose estimator are quite satisfactory, however, the pose estimator algorithm has an ambiguity: we know the amplitude of \(R_{x}\) and \(R_{y}\) estimated angles but we don't know the signal. That is, when the camera is positioned in relation to the target as shown in Figure 21, it exists an ambiguity in the signal for the \(R_{x}\) and \(R_{y}\) rotations, however, in the context of our work, this is an ambiguous situation that does not happen considering the way the sea-floor docking station is approached (see Figures 1 and 3) by a hovering AUV. This situation can be resolved by adding one more marker to the target. In this case, this fourth marker should be non-coplanar with the other three. In terms of robustness of relative pose estimates, we recognize that there would be an advantage in fusing the monocular vision information with inertial rate sensor measurements to generate an estimate of the relative position between a moving observer and a stationary object. This fusion would create redundancy and therefore increase the robustness of the estimates. That will be a future line of work.