RGB-D Mapping: Using Depth Cameras for Dense 3D Modeling of Indoor Environments
   Peter Henry, Mike Krainin, Evan Herbst, Xiaofeng Ren, and Dieter Fox, at ISER 2010


RGB-D cameras are novel sensing systems that capture RGB images along with per-pixel depth information. In this paper we investigate how such cam- eras can be used in the context of robotics, speci.cally for building dense 3D maps of indoor environments. Such maps have applications in robot navigation, manip- ulation, semantic mapping, and telepresence. We present RGB-D Mapping, a full 3D mapping system that utilizes a novel joint optimization algorithm combining visual features and shape-based alignment. Visual and depth information are also combined for view-based loop closure detection, followed by pose optimization to achieve globally consistent maps. We evaluate RGB-D Mapping on two large indoor environments, and show that it effectively combines the visual and shape informa- tion available from RGB-D cameras.