3D visual SLAM for an assistive robot in indoor environments using RGB-D cameras

With a growing global aging population, assistive robots are becoming increasingly important. This paper presents an integrated hardware and software architecture for assistive robots. This modular and reusable software framework incorporates capabilities of perception and navigation. The paper presents as well a system for three-dimensional (3D) vision-based simultaneous localization and mapping (SLAM) using a Red-Green-Blue and Depth (RGB-D) camera, and illustrates its application on an assistive robot. The ORB features and depth information are extracted for ego-motion estimation. Random Sample Consensus algorithm (RANSAC) is adopted for outlier removal, while the integration of RGB-D and iterated closest point (ICP) is used for alignment. Pose-graph optimization is completed by g2o. Finally, a 3D volumetric map is generated for further navigation.