2D based Indoor SLAM and Autonomous Navigation using a Terrain ROBOT
RGB-D cameras (such as the Microsoft Kinect) are novel sensing systems that capture RGB images along with per-pixel depth information. In this paper we investigate how such cameras can be used for building dense 2D maps of indoor environments. Such maps have applications in robot navigation, manipulation, semantic mapping, and telepresence. We present RGB-D Mapping, a full 2D 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 indoor environments, and show that it effectively combines the visual and shape information available from RGB-D cameras. In the second part of the paper we develop a reactive navigation system in which a mobile robot moves avoiding obstacles in environment, using the distance sensor Kinect.