PLP-SLAM: Point-Line-Plane Simultaneous Localization and Mapping

For indoor environments, prior point-based visual SLAM cannot be processed in real time under low texture and illumination. To address this issue, this work proposes PLP-SLAM (Point-Line-Plane-SLAM) with RGB-D camera. Firstly, point and line features are detected in RGB images. For line features, establish length suppression and near line merge strategy to improve the line extraction quality. Secondly, plane features are extracted based on agglomerative hierarchical clustering method in point cloud obtained by RGB-D camera. Point clouds are divided into several nodes, unlike prior methods spend a lot of time to estimate the normal vector for each individual point, this work assumes that points within each node sharing the same plane normal vector, which can significantly improve the computational efficiency. Thirdly, sparse maps including points, lines and planes are established, meanwhile the scenes are reconstructed by creating the dense maps to show plan features directly. Finally, the performance of proposed method is compared against the state-of-the-art SLAM on public datasets to evaluate the pose estimation. All modules are run in real-time on a CPU, experiments clarify that PLP-SLAM can significantly enhance the robustness of 6DoF pose of the camera and simultaneously creating more detailed maps of the environment.