Fig. 2: Stereo vision system architecture.
of the calibration and rectification steps, we typically get a RMS re-projection error of the input calibration points
of ERMS = 0.2 pixel. After rectification of the left and right images, the relationship between the depth Z and the
binocular disparity d is given by:
Z = f
S pix
T
d (1)
where Z is given in meter, d is given in pixel, f is the lenses focal length, S pix is the pixel size and T is the system
baseline. The stereo vision system default configuration features low distortion lenses with a focal length of 5.5mm.
Considering that the pixel size is 5.6um the maximum working Zmax range of the stereo system will be Zmax ≈ 50m.
The minimum working range will be limited by the disparity range allowed for the stereo matching. The closer the
object will be to the camera, the higher the binocular disparity. Depending on the complexity of the stereo matching
algorithm, a tradeoff should be made between the matching process complexity, the disparity range, the field of view
and the processing speed. In our software implementation we typically set the maximum binocular disparity possible
to 128 pixels. This gives a minimum working range Zmin ≈ 0.38m. We can also compute the accuracy Zacc of our
calibrated and rectified system as a function of the RMS re-projection error and binocular disparity:
Zacc = ERMS
f
S pix
T
d2 (2)
By combining eq. 1 and eq. eq. 2 we can compute the depth accuracy as a function of depth:
Zacc = ERMS
S pix
f T
Z2 (3)
Figures 3a and 3b gives the short range and long range accuracy curves for ERMS = 0.2. Those curves should be
seen as the best accuracy that the stereo camera can achieved. Obviously, the absolute depth precision will dependent
on the performance of the stereo matching algorithm. In the case of an active system, the presence of an illuminator
generating a pattern will also help to achieve the accuracy given by fig. 3a and fig. 3b. Figure 4 gives some depth
map sample images computed from the rectified left and right images using the stereo matching algorithm described
in5 without any kind of illuminator or pattern generator.
4. Conclusion
In this paper, we have presented a stereo vision system that relies on 2 logarithmic WDR 1280x720 sensors.
This stereo vision system is able to provide high quality contrast indexed images of highly contrasted scene. This
contrast indexed sensing capability can be conserved over more than 140dB without any explicit sensor control and
without delay. The logarithmic photoresponse is also interesting if we consider the image robustness to illumination
variabilities. This dynamic range and contrast conservation makes this stereo camera very effective for outdoor 3D
stereo vision applications. Besides it also gives the unique possibility to combine passive 3D approaches with active
illumination approaches without fear of losing information because of saturation