A predictive object detection algorithm was developed to investigate the practicality of using advanced filtering on
stereo vision object detection algorithms such as the X-H Map. Obstacle detection with stereo vision is inherently
noisy and non linear. This paper describes the X-H Map algorithm and details a method of improving the accuracy
with the Unscented Kalman Filter (UKF). The significance of this work is that it details a method of stereo vision
object detection and concludes that the UKF is a relevant method of filtering that improves the robustness of obstacle
detection given noisy inputs. This method of integrating the UKF for use in stereo vision is suitable for any standard
stereo vision algorithm that is based on pixel matching (stereo correspondence) from disparity maps.
KEYWORDS: 3D modeling, 3D vision, Visual process modeling, Optical spheres, 3D image processing, 3D metrology, Spherical lenses, Cameras, Motion models, Commercial off the shelf technology
Three dimensional visual recognition and measurement are important in many machine vision applications. In some cases, a stationary camera base is used and a three-dimensional model will permit the measurement of depth information from a scene. One important special case is stereo vision for human visualization or measurements. In cases in which the camera base is also in motion, a seven dimensional model may be used. Such is the case for navigation of an autonomous mobile robot. The purpose of this paper is to provide a computational view and introduction of three methods to three-dimensional vision. Models are presented for each situation and example computations and images are presented. The significance of this work is that it shows that various methods based on three-dimensional vision may be used for solving two and three dimensional vision problems. We hope this work will be slightly iconoclastic but also inspirational by encouraging further research in optical engineering.
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