RAS: Recursive automotive stereo

Abstract

Obstacle avoidance is a key feature for automotive navigation that requires an accurate representation of the environment. In the field of visual perception this task has often been addressed with stereo algorithms that try to obtain a depth map of the environment via disparity calculations on a single pair of images. These algorithms do not exploit that especially in automotive scenarios the fields of view between two consecutive frames have large overlapping regions. Instead, the disparity map is computed from scratch for each stereo frame and no information is propagated from one frame to the next. Since monocular image processing has long benefited from recursive estimation techniques, such as the 4D Approach, this paper presents a novel recursive automotive stereo algorithm, called RAS. RAS internally maintains a list of recursively estimated 3D points that are continuously updated based on the vehicle's movement and measurements in the current stereo frame. We show that RAS not only preserves the knowledge of the environment across frames, but also accounts for measurement modalities and is robust against faulty or even missing measurements.