Stereo without depth search and metric calibration

We propose a new stereo method for 2D navigation in a dynamic environment such as roads without depth search and metric camera calibration. Conventionally, there is an effective stereo method based on the constraint that an observer moves on the ground plane, namely the GP constraint. Although the GP constraint is often violated in an outdoor environment due to the movement of the observer, i.e., camera vibrations and inclination, it can be updated using some features on the ground plane only if the stereo cameras are weakly calibrated. However, the conventional stereo method has several drawbacks. First, it is rather difficult to solve for the general epipolar geometry summarized in the fundamental matrix. Second, although the updated GP constraint often becomes imperfect, it lacks an effective contrivance for the noise reduction. Third, there is no measure to assess the danger of detected obstacles. To solve these problems, we develop a domain-specific stereo method which utilizes various attributes of roads. We introduce the "pseudo-projective camera model" that provides a good approximation to the projective camera in road scenes and accordingly define the linear epipolar geometry for a pair of pseudo-projective cameras. Furthermore, we show that extraction of two parallel lines lying on the road is effective to update the GP constraint, overcome the noise issue and even to estimate the degree of danger. Through experiments we demonstrate that our method is efficient and applicable to a variety of outdoor scenes.