Dense disparity features for fast stereo vision

Abstract

A novel stereo vision algorithm suitable for real-time autonomous robot applications is proposed. The method extracts dense segments of constant disparity using a similarity metric based on the sum of absolute differences. The resolution of matching image segments can be defined adaptively to allow variable detail. The algorithm is tested on reference data-sets and self captured images and is shown to produce promising results. Handling of regions without texture is also discussed.