Depth Estimation using Stereo Fish-Eye Lenses

This paper presents the estimation of depth in an indoor, structured environment based on a stereo setup consisting of two fish-eye lenses, with parallel optical axes, mounted on a robot platform. The use of fish-eye lenses provides for a large field of view to estimate better the depth of features very close to the lens. To extract significant information from the fish-eye lens images, we first correct for the distortion before using a special line detector, based on vanishing points, to extract significant features. We use a relaxation procedure to achieve correspondence between features in the left and right images. The process of prediction and recursive verification of the hypotheses is utilized to find a one-to-one correspondence. Experimental results obtained on several stereo images are presented, and an accuracy analysis is performed. Further, the algorithm is tested using a pair of wide-angle lenses, and the accuracy and difference in the spatial information obtained are compared.