A Stereovision Method for Obstacle Detection and Tracking in Non-Flat Urban Environments

Authors:
Qian Yu;Helder Araújo;Hong Wang
Affiliations:
State Key Laboratory of Intelligent Technology and Systems, Tsinghua University, Beijing, China;Department of Elect. and Computer Eng.--Polo II, Institute of Systems and Robotics, University of Coimbra, Coimbra, Portugal 3030-290;State Key Laboratory of Intelligent Technology and Systems, Tsinghua University, Beijing, China
Venue:
Autonomous Robots
Year:
2005

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Abstract

Obstacle detection is an essential capability for the safe guidance of autonomous vehicles, especially in urban environments. This paper presents an efficient method to integrate spatial and temporal constraints for detecting and tracking obstacles in urban environments. In order to enhance the reliability of the obstacle detection task, we do not consider the urban roads as rigid planes, but as quasi-planes, whose normal vectors have orientation constraints. Under this flexible road model, we propose a fast, robust stereovision based obstacle detection method. A watershed transformation is employed for obstacle segmentation in dense traffic conditions, even with partial occlusions, in urban environments. Finally a UKF (Unscented Kalman filter) is applied to estimate the obstacles parameters under a nonlinear observation model. To avoid the difficulty of the computation in metric space, the whole detection process is performed in the disparity image. Various experimental results are presented, showing the advantages of this method.