Recursive 3D scene estimation with multiple camera pairs

Abstract

In this paper we present the recursive estimation of static scenes with multiple stereo camera pairs. The estimation is based on a point cloud created from the disparities of the cameras. The focus lies on reducing erroneous measurements while obtaining a comparatively dense measurement in real time. While recursive scene estimation via stereo cameras has been presented several times before, the estimation has never been exploited in the measurement algorithm. We propose the usage of the current scene estimation in the disparity measurement to increase robustness, denseness and outlier rejection. A scene prior is created for each measurement using OpenGL taking occlusions, camera positions and existence probability into account. Additionally, multiple stereo pairs with different alignment provide distinct information. Each disparity measurement benefits from the complete scene knowledge the other stereo camera pairs provide. The creation of new points for the point cloud is based on a scaled version of the current scene and allows for simple trade-off between computational effort and point cloud denseness.