Perception in Disparity: An Efficient Navigation Framework for Autonomous Vehicles With Stereo Cameras

Abstract

Stereo cameras are widely used in autonomous vehicles as environmental perception sensors because of their availability and low cost. However, efficiently utilizing the obtained disparity images to generate a desirable local path for the vehicle still remains a challenging problem. In this paper, we present a novel navigation framework for autonomous vehicles equipped with stereo cameras, featuring finishing all of the perception and path planning tasks directly within the disparity space. Comparing with the popular 3-D counterpart, disparity is a projected geometric space that contains more primitive information directly computed from the stereo images. Furthermore, disparity image is a more compact representation for large field of 3-D Cartesian space, which makes perception and path finding in longer distance possible. The proposed framework is composed of three modules, namely, local disparity map building, slope analysis and obstacle detection, and path planning. Two important properties concerning the motion and slope in disparity space are presented for the first time, i.e., the motion model and the slope model in disparity space. With the motion model, the framework first fuses consecutive disparity maps to construct a more reliable and complete local map. Then, a novel slope analysis method called V-Intercept is developed based on the slope model. It can efficiently analyze slopes and obstacles, generating a reasonable cost map directly from the disparity image. Finally, the obstacles in the cost map are expanded properly, and a customized A* search algorithm is performed to find a reasonable path in disparity space. The experimental results show that our framework works well under various kinds of environments. The resulting system can efficiently perceive and plan on a much larger range and react to obstacles further beyond the traditional Cartesian-based method.