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An approach to stereo based local path planning in unstructured environments is presented. The approach differs from previous stereo based and image based planning systems (e.g. top-down occupancy grid planners, autonomous highway driving algorithms, and view-sequenced route representation), in that it uses specialized cost functions to find paths through an occupancy grid representation of the world directly in the image plane and forgoes a projection of cost information from the image plane down onto a top-down 2D Cartesian cost map. We discuss three cost metrics for path selection in image space. We present a basic image based planning system, discuss its susceptibility to rotational and translational oscillation, and present and implement two extensions to the basic system that overcome these limitations - a cylindrical based image system and a hierarchical planning system. All three systems are implemented in an autonomous robot and are tested against a standard top-down 2D Cartesian planning system on three outdoor courses of varying difficulty. We find that the basic image based planning system fails under certain conditions; however, the cylindrical based system is well suited to the task of local path planning and for use as a high resolution local planning component of a hierarchical planning system.