Noise-resilient road surface and free space estimation using dense stereo

This paper proposes a noise-resilient road surface and free space estimation method using dense stereo. The proposed road surface estimation method selects 3D points expected to compose a road surface using YZ-plane accumulation, and then finds the road surface by sequentially estimating a piece-wise linear function based on a RANSAC framework. This makes our method insensitive to 3D points on obstacles and stereo matching errors on textureless road regions. The proposed free space estimation method is based on the fact that disparities from roads and obstacles should be equal at the free space boundary. This method calculates disparity consistency between road and obstacle surfaces, and finds free space that gives the best disparity consistency and depth smoothness using dynamic programming. This approach achieves robustness against stereo matching errors on obstacle surfaces and objects located in the air since its estimation process is independent of disparity accumulation unlike previous occupancy grid-based method. The experimental results show that the proposed method is able to estimate road surfaces and free spaces in various severe situations.