GLOSARIO TÉCNICO (A)

In [Malis 02f] we proposed a new class of visual servoing schemes that encompasses several image-based visual servoing approaches such as [Malis 99, Basri 99]. The new class of visual servoing schemes is based on the reconstruction of the pose (up to a scale factor for the translation) of the camera from two images. The key idea of the new class

is to estimate the rotation from two views. Therefore the rotation is directly controlled and decoupled from the control of the translation. The translation can be controlled in several different ways which differentiate between the possible methods within the class. This class of methods has interesting properties. Since the rotation control is decoupled from the translation control, it is possible to ”easily” study the stability and robustness of the control law with respect to errors on the intrinsic and extrinsic camera parameters (the extrinsic parameters are the pose of the camera with respect to the robot end- effector) [Malis 01a, Malis 02f]. Again, the analysis of the robustness of the control law is very important since it allowed us to show that the ”size”of the calibration error that the control laws can tolerate is very large.

The new class of visual servoing schemes is based on the assumption that we can estimate the rotation matrix directly from the current and reference images. In the more general case (i.e. both for non-planar and planar objects), the rotation can be extracted from the homography matrix (see Chapter 3). Theoretically, the homography matrix can be estimated from any sufficient visual information (e.g. a set of points, of lines, ...). For example, we proposed in [Chesi 00] a complete vision-based control system with respect to planar contours. In the system we integrated the visual matching [Chesi 99], the visual tracking [Drummond 99] and the visual servoing [Malis 99]. However, this integration work highlighted two problems in our approach. First of all, the feature-based estimation for the task function was specific to planar contours. A different system should be built for different features. Secondly, it is impossible to compute the rotation from the homography matrix using image data only (the current and reference image). Indeed, there are two possible solutions when we decompose an homography matrix. This second problem led us to propose a different vision-based control method that is detailed in the next section. The solution to the first problem has been to estimate the homography directly from image data without any feature extraction [Malis 04d, Malis 05]. We use the ESM for the direct image registration of a planar surface (see Section 3). This allowed us to simplify the design of the visual servoing approach and to increase its flexibility since the planar surface can contain any information. Figures 4.3 and 4.4 show a vision-based car-platooning experiment that is performed in a real outdoor environment.

Figure 4.3 gives an overview of the system while Figure 4.4 illustrates more details of the experiment. Two electric vehicles of type ”Cycab” are used : one as a guider car and the other as a follower car. A driver guides the first car while the follower car is controlled by a position-based control scheme. The control scheme takes into account that the vehicle is non-holonomic and tries to keep the distance between the two vehicles constant and equal to the initial distance. The relative position is given by the ESM visual tracking system. The pan-tilt turret is controlled in order to keep the guider car in the field of view of the camera during the experiment.

In the starting situation, when the guider car is in front of the follower car, a window of (100×100) pixels is selected to be the reference pattern. In order to have a metric reconstruction, the camera was roughly calibrated and the distance between the two cars is given to the control process. It is the distance between the camera of the follower car and a poster stacked on the back windshield of the guider car. Tracking this reference pattern provides the relative position between the two vehicles. The blue square indicates the tracked region. In the right column, the reprojections of the tracked region using the estimated homographies are shown. The first row of the figure corresponds to the initial position. The ESM tracking algorithm performs well although the experiment takes place outdoors and sun reflection on the tracked region occurs. The current pattern reprojections are very close to the reference one. More details on the experiment can be found in [Benhimane 05].

Low−level wheel steering control Low level velocity control

CAN bus controller

Wheel steering control Velocity control Serial comm. RS232 Tracking Software ESM Vision Frame grabber Pan−Tilt turret CCD Camera

Computer 2 − Pentium III 700 MHz Computer 1 − Pentium III 700 MHz CAN bus comm. process

TCP/IP socket comm. process

Fig. _{4.4 – Several images of a platooning application. The first column shows the red} car automatically following the leader. The second column shows the area of interest in the current image. The third column shows the registered images that prove that the homography has been correctly estimated.