Feature-based SLAM for Dense Mapping

Abstract

An improved feature-based SLAM for dense mapping is proposed in this paper to easy follow-up works of robots. Features are extracted by original ORB-SLAM for pose estimation. Filter is used to get gradient graph for each image and points whose gradient are higher than threshold is added as feature points, then a voxel filter is adopted to reduce sampling. Maps are built by method as in ORB-SLAM but denser. Experimental results demonstrate the improved feature-based SLAM in this paper reconstructs scenes well with dense maps with acceptable CPU usage and efficiency.