A real-time dense stereo matching method for critical environment sensing in autonomous driving

This paper proposes a novel real-time stereo matching algorithm paired with an online auto-rectification framework to solve environment sensing problems in autonomous driving, such as road, curb and small object detection etc. The algorithm treats disparities of stereo images as hidden states and conducts a Viterbi process at 4 bi-directional paths to estimate them. Structural similarity, total variation constraint, and a specific hierarchical merging strategy are combined with the Viterbi process to improve the robustness and accuracy. Based on the results of Viterbi, a convex optimization equation is derived to estimate epipolar line distortion. The estimated distortion information is used for the online compensation of Viterbi process at an auto-rectification framework. The stereo matching result is used to generate the histogram map. A Radon transform and a Viterbi process are applied to the histogram map to detect roads and curbs. Disparities near the road are mapped to 3D space to detect small objects on road. Real-word experiments show that this method can help sensing the surrounding 3D environment for driving vehicles robustly.