3D-segmentation of traffic environments with u/v-disparity supported by radar-given masterpoints

Abstract

3D-segmentation of a traffic scene with two-dimensional row- and column-disparity-histograms, namely u/v-disparities, has become more and more popular for modern stereo-camera-based driver assistance systems due to its fast computation in real-time, few memory requirements and robustness against noisy or intermittent data. In this paper, we present a novel approach to support this pure vision-based method by projecting preprocessed radar-signals directly to u-disparity-space. We called the projection result “masterpoints”. This data fusion on low feature-level improved the segmentation process and increased the obstacle detection rate significantly. No assumptions about obstacle-type or -size are needed. Furthermore, the algorithms can be parallelized easily and run in real-time.