## Vision-Based Sensing
### Marker Detection  
In order to measure the current robot position visually, circular-shaped orange markers with a diameter of 4 cm are attached to three decisive points on the robot, see Figure 1. The marker detection can be realized based on different criteria. The first option is a color-based detection, where the desired objects are identified with the help of color filters [23; 24]. The second option is a shape-based detection, that is, to identify circles in the image. For this, a variant of the Hough transform or a Sobel filter can be used as in Reference [27]. Another option would be the comparison of the current image with a reference image [37]. By computing the difference of both images, moving objects can be detected.  
The latter method based on image differences is not suitable here, since not only the three markers but all the moving parts of the robot would be detected. For a shape-based marker detection, first edges have to be detected, for example by means of the Canny algorithm [38], before the circles can be identified using the Hough transform [39]. Since this process takes about two seconds, the markers are tracked, for example, with the Kanadi-Lucas-Tomasi method (KLT tracking) [40; 41] after they have been identified once. Unfortunately, often points get lost after a few seconds, as experiments show, and the markers have to be identified once again, which takes too long for real-time execution. Therefore, the shape-based detection method is unsuitable here as well.  
The detection method chosen for the application presented in this paper is color-based. The images are analyzed in HSV color space, which is short for _lue_, _saturation_, and _value_. Hue stands for the color, for example, red, green, or blue, saturation indicates the colorfulness, and value corresponds to the brightness. The HSV color space is particularly suitable, since different shades are to be found coherently in one sector of the color space and can therefore be described independently from the brightness. The brightness of the ambient light thus has only little influence on the result.  
The individual steps of the color filtering process are visualized in Figure 8.  
The filter parameters have to be adjusted such that the areas of the original image, see Figure 8a, which contain a marker, pass the filter completely, while the environment is filtered out as accurately as possible. Thereby, a binary image, see Figure 8b, is created, which still contains some artifacts though, that is, pixels of the environment that passed the filter erroneously. To get rid of those, the  
Figure 8: Step-by-step detection of orange markers in an image of the robot by application of color filtering and morphological opening.  
morphological operation called opening is applied [42], yielding the image in Figure 8c, which contains exactly the desired markers. The center points of the remaining areas are the marker coordinates.  
One-time at the beginning, the identified image coordinates have to be assigned to the corresponding markers. Afterwards, the matching can be done automatically using shortest distances to marker positions from the preceding image.  
The main advantage of this method is on the one hand the significantly lower computation time compared to shape-based detection by Hough transform. On the other hand, it is still slower than KLT tracking. However, in total the advantages of color-based detection prevail. It allows a reliable detection of the markers during the complete period of robot movement with a time step size still suitable for real-time application.