Efficient Algorithms for Obstacle Detection Using Range Data

Abstract Algorithms are presented which segment range images and classify regions as being navigable or unnavigable by a land vehicle. The partial derivatives in spherical coordinates of range images are shown to yield rapid and robust results for obstacle detection. The algorithms are applied to data collected from an active laser range sensor mounted on an autonomous land vehicle and their comparative results are analyzed. The sensitivity of various algorithms to uncertainty in the orientation of the range sensor are studied. Experiments on image enhancement, including iterative symmetric-nearest-neighbor smoothing and mixed-pixel minimization, are also presented.