A Coarse and Relevant 3D Representation for Fast and Lightweight RGB-D Mapping

In this paper we present a novel lightweight and simple 3D representation for real-time dense 3D mapping of static environments with an RGB-D camera. Our approach builds and updates a low resolution 3D model of an observed scene as an unordered set of new primitives called supersurfels, which can be seen as elliptical planar patches, generated from superpixels segmented RGB-D live measurements. While most of the actual solutions focuse on the accuracy of the reconstructed 3D model, the implemented method is well-adapted to run on robots with reduced/limited computing capacity and memory size, which do not need a highly detailed map of their environment but can settle for an approximate one.