RGB-D SLAM in Indoor Environments With STING-Based Plane Feature Extraction

In this paper, the RGB-D camera-based simultaneous localization and mapping (SLAM) of indoor environments is addressed using plane features. The plane parameter space (PPS) is defined for a compact representation of planes in the Cartesian space. The statistical information grid (STING) structure is constructed in the PPS to extract plane features. The plane association graph is developed to determine the correspondences between the plane features from two successive scans. The RGB-D camera pose is directly calculated using the matched plane features if they can provide sufficient constraints for the pose estimation. Otherwise, a novel STING-based scan matching method is developed in the PPS to achieve a full six degrees of freedom camera pose estimation. The proposed method uses only the plane features independent of any other features to estimate the RGB-D camera poses and can thus be suitable for some challenging scenes. The experimental results demonstrate that the proposed plane feature-based RGB-D SLAM can achieve high accuracy and robustness in both on-board and hand-held applications.