<title>Vision-based real-time obstacle detection and tracking for autonomous vehicle guidance</title>

Abstract

The ability of obstacles detection and tracking is essential for safe visual guidance of autonomous vehicles, especially in urban environments. In this paper, we first overview different plane projective transformation (PPT) based obstacle detection approaches under the planar ground assumption. Then, we give a simple proof of this approach with relative affine, a unified framework that includes the Euclidean, projective and affine frameworks by generalization and specialization. Next, we present a real-time hybrid obstacle detection method, which combined the PPT based method with the region segmentation based method to provide more accurate locations of obstacles. At last, with the vehicle's position information, a Kalman Filter is applied to track obstacles from frame to frame. This method has been tested on THMR-V (Tsinghua Mobile Robot V). Through various experiments we successfully demonstrate its real-time performance, high accuracy, and high robustness.