Predictive vision from stereo video: robust object detection for autonomous navigation using the Unscented Kalman Filter on streaming stereo images

Donald Rosselot; Mark Aull; Ernest L Hall

doi:10.1117/12.839243

18 January 2010 Predictive vision from stereo video: robust object detection for autonomous navigation using the Unscented Kalman Filter on streaming stereo images

Donald Rosselot, Mark Aull, Ernest L Hall

Author Affiliations +

Proceedings Volume 7539, Intelligent Robots and Computer Vision XXVII: Algorithms and Techniques; 75390R (2010) https://doi.org/10.1117/12.839243
Event: IS&T/SPIE Electronic Imaging, 2010, San Jose, California, United States

Abstract

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.

Citation Download Citation

Donald Rosselot, Mark Aull, and Ernest L Hall "Predictive vision from stereo video: robust object detection for autonomous navigation using the Unscented Kalman Filter on streaming stereo images", Proc. SPIE 7539, Intelligent Robots and Computer Vision XXVII: Algorithms and Techniques, 75390R (18 January 2010); https://doi.org/10.1117/12.839243

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