Spatial uncertainty model of a three-view RGB-D camera system

Abstract

Multi-view stereo cameras and RGB-D cameras are widely used in robotic vision for 3D map reconstruction in navigation tasks. RGB-D cameras provide accurate depth measurements even in textureless areas, but are sensitive to distortion of its actively projected patterns. Stereo cameras are reliable if and only if there are sufficient features in the visible region. The two kinds of sensors are complementary in performance, so we combine them to a three-view RGB-D system and propose a fusion method for reliable 3D point cloud reconstruction. Furthermore, the reliability of the reconstructed map is vital for robotic navigation, so we build a spatial uncertainty model for the system, which can be easily specialized to either subsystems. The fusion method is shown to have gain in performance from the spatial uncertainty perspectives.