Map construction algorithm based on dense point cloud

Abstract

Abstract In order to address the issue of the difficulty in constructing maps from sensor‐acquired point clouds, a map construction algorithm is proposed for the effective conversion from sparse point clouds to dense point clouds. First, it constructs a dense point cloud map in real‐time using colour and depth information of keyframes. Then, it generates an accurate point cloud map by voxel filtering and coordinate conversion. Finally, the point cloud map is converted into an octree map using OctoMap. This article uses the TUM dataset for simulation analysis to verify the effectiveness of the algorithm. The results demonstrate that the algorithm can construct dense point cloud maps with high positioning accuracy in sparse point cloud scenarios. The algorithm improves the positioning accuracy by more than 15% under the fr1_xyz, fr1_room, fr1_desk2, fr1_desk1, fr2_desk and fr3_str_tex_near sequences, compared to ORB‐SLAM2, ORB‐SLAM3 and GX ORB‐SLAM2 algorithm

Keywords

• Simultaneous Localization and Mapping (SLAM)
• Dense point cloud maps
• Voxel filtering
• Octree maps