This paper presents a self-positioning system for the mobile robot. The proposed positioning system consists of a stereo setup of two ODVs (omni-directional vision) for obtaining a panoramic image and a disparity image. In general, images obtained by such a device have a relatively low spatial resolution and have some aberration in comparison to standard camera images and thus, considerable range estimation errors are caused. We propose a stereo set up of an ODV, which features two mirrors to minimize blurring influence. We also propose an edge-based intensity interpolation method, a bidirectional sub-pixel matching procedure, and a discontinuity compensation algorithm to reduce disparity error. The relative self-position and the heading angle can be estimated from the direction and the distance toward two landmarks. Comparison between the theoretical positioning accuracy based on the Cramer-Rao lower band and the experimental result in the field prove that both the ODV, the proposed stereo matching algorithm and the self-positioning algorithm are valid and effective.

This paper presents the estimation of depth based on the cylindrical re-projections of images captured by a stereo setup consisting of two sets of an ODV (omnidirectional vision system). In general, images obtained by such a device have a relatively low spatial resolution and have some aberration in comparison to standard camera images and thus, considerable range estimation errors are caused. We propose a stereo setup of an ODV, which features two mirrors with a contrived curvature to minimize blurring influence. We also propose an edge-based intensity interpolation method, a bi-directional sub-pixel matching procedure, and a discontinuity compensation algorithm to reduce the disparity error.