This paper presents a novel technique which actually restructures the 3D image profiled by stereovision method. Correspondence between two images is addressed by projecting a 2D fringe pattern. These projected patterns fix their positions to the tested object during two segmented measurements. Finding two matched surface points becomes a problem of searching for two identical phases in the fused data sets. The proposed method is superior to the other methods because of the following reasons: (1)We successfully designed a 2-D fringe pattern whose the transmittance is sinusoidal and the accuracy of the sinusoidal fringe pattern was of the order of sub-microns. (2)By using the 2-D fringe pattern, image registration was achieved more easily. (3)By using the optical spot method, the 3d image profiled was restructured such that the computational time was reduced and the crabwise accuracy was better than that of a single CCD system.

At the heart of every stereo vision algorithm is a solution to the matching problem - the problem of finding points in the right and left image that correspond to a single point in the real world. Applying assumptions regarding the epipolar rectification and color similarity between two frames is often not possible for real-world image capture systems, like those used in urban search and rescue robots. More flexible and robust feature descriptors are necessary to operate under harsh real world conditions. This thesis compares the accuracy of disparity images generated using local features including points, line segments, and regions, as well as a global framework implemented using loopy belief propagation. This thesis will introduce two new algorithms for stereo matching using line segments and regions, as well as several support structures that optimize the algorithms performance and accuracy. Since few complete frameworks exist for line segment and region features, new algorithms that were developed during the research for this thesis will be outlined and evaluated. The comparison includes quantitative evaluation using the Middlebury stereo image pairs and qualitative evaluation using images from a less structured environment. Since this evaluation is grounded in urban search and rescue robotics, processing time is a significant constraint which will be evaluated for each algorithm. This thesis will show that line segment-based stereo vision with a gradient descriptor achieves at least a 10% better accuracy than all other methods used in this evaluation while maintaining the low runtime associated with local feature based stereo vision.

At the heart of every stereo vision algorithm is a solution to the matching problem - the problem of finding points in the right and left image that correspond to a single point in the real world. Applying assumptions regarding the epipolar rectification and color similarity between two frames is often not possible for real-world image capture systems, like those used in urban search and rescue robots. More flexible and robust feature descriptors are necessary to operate under harsh real world conditions. This thesis compares the accuracy of disparity images generated using local features including points, line segments, and regions, as well as a global framework implemented using loopy belief propagation. This thesis will introduce two new algorithms for stereo matching using line segments and regions, as well as several support structures that optimize the algorithms performance and accuracy. Since few complete frameworks exist for line segment and region features, new algorithms that were developed during the research for this thesis will be outlined and evaluated. The comparison includes quantitative evaluation using the Middlebury stereo image pairs and qualitative evaluation using images from a less structured environment. Since this evaluation is grounded in urban search and rescue robotics, processing time is a significant constraint which will be evaluated for each algorithm. This thesis will show that line segment-based stereo vision with a gradient descriptor achieves at least a 10% better accuracy than all other methods used in this evaluation while maintaining the low runtime associated with local feature based stereo vision.

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This thesis presents an indoor obstacle avoidance system for car-like mobile robot. The system consists of stereo vision, map building, and path planning. Stereo vision is performed on stereo images to create a geometric map of the environment. A fast sparse stereo approach is employed. For different areas of the image there are different optimal values of disparity range. A multi-pass method to combine results at different disparity range is proposed. To reduce computational complexity the matching is limited to areas that are likely to generate useful data. The stereo vision system outputs a more complete disparity map. Abstract Map building involves converting the disparity map into map coordinates using triangulation and generating a list of obstacles. Occupancy grids are built to aid a hierarchical collision detection. The fast collision detection method is used by the path planner. Abstract A steering set path planner calculates a path that can be directly used by a car-like mobile robot. An adaptive approach using occupancy grid information is proposed to improve efficiency. Using a non-fixed steering set the path planner spends less computation time in areas away from obstacles. The path planner populates a discrete tree to generate a smooth path. Two tree population methods were trialled to execute the path planner. The methods are implemented and experimented on a real car-like mobile robot.

We report on experiments with a mobile robot using one vision process (forward motion vision) to calibrate another (stereo vision) without resorting to any external units of measurement. Both are calibrated to a velocity dependent coordinate system which is natural to the task of obstacle avoidance. The foundations of these algorithms, in a world of perfect measurement, are quite elementary. The contribution of this work is to make them noise tolerant while remaining simple computationally. Both the algorithms and the calibration procedure are easy to implement and have shallow computational depth, making them (1) run at reasonable speed on moderate uni-processors, (2) appear practical to run continuously, maintaining an up-to-the-second calibration on a mobile robot, and (3) appear to be good candidates for massively parallel implementations.

It is commonly assumed that the goal of stereovision is computing explicit 3D scene reconstructions. We show that very accurate camera calibration is needed to support this, and that such accurate calibration is difficult to achieve and maintain. We argue that for tasks like recognition, figure/ground separation is more important than 3D depth reconstruction, and demonstrate a stereo algorithm that supports figure/ground separation without 3D reconstruction.

The computation of texture and of stereoscopic depth is limited by a number of factors in the design of the optical front-end and subsequent processing stages in humans and machines. A number of limiting factors in the human visual system, such as resolution of the optics and opto-electronic interface, contrast, luminance, temporal resolution and eccentricity are reviewed and evaluated concerning their relevance for the recognition of texture and stereoscopic depth. The algorithms used by the human brain to discriminate between textures and to compute stereoscopic depth are very fast and efficient. Their study might be beneficial for the development of better algorithms in machine vision.

A mobile robot must be able to determine its own position in the world. To support truly autonomous navigation, we present a system that builds and maintains its own models of world locations and uses these models to recognize its world position from stereo vision input. The system is designed to be robust with respect to input errors and to respond to a gradually changing world by updating the world location models. We present results from tests of the system that demonstrate its reliability. The model builder and recognition system fit into a planned world modeling system that we describe.

Obtaining exact depth from binocular disparities is hard if camera calibration is needed. We will show that qualitative depth information can be obtained from stereo disparities with almost no computations and with no prior knowledge (or computation) of camera parameters. We derive two expressions that order all matched points in the images in two distinct depth-consistent ways from image coordinates only. One is a tilt-related order $\\lambda$, the other is a depth-related order $\\chi$. Using $\\lambda$ demonstrates some anomalies and unusual characteristics that have been observed in psychophysical experiments. The same approach is applied to qualitatively estimate changes in the curvature of a contour on the surface of an object, with either $x$- or $y$-coordinate fixed.