Building maps for autonomous navigation using sparse visual SLAM features

Autonomous navigation, which consists of a systematic integration of localization, mapping, motion planning and control, is the core capability of mobile robotic systems. However, most research considers only isolated technical modules. There exist significant gaps between maps generated by SLAM algorithms and maps required for motion planning. This paper presents a complete online system that consists in three modules: incremental SLAM, real-time dense mapping, and free space extraction. The obtained free-space volume (i. e. a tessellation of tetrahedra) can be served as regular geometric constraints for motion planning. Our system runs in real-time thanks to the engineering decisions proposed to increase the system efficiency. We conduct extensive experiments on the KITTI dataset to demonstrate the run-time performance. Qualitative and quantitative results on mapping accuracy are also shown. For the benefit of the community, we make the source code public.

Authors

• Yonggen Ling
• Shaojie Shen

Keywords

• Simultaneous localization and mapping
• Cameras
• Planning
• Optimization
• Sparse Feature
• Autonomous Navigation
• Qualitative Results
• Density Map
• Accurate Mapping
• Path Planning
• KITTI Dataset
• Runtime Performance
• 3D Reconstruction
• Global Optimization
• Point Cloud
• Local Map
• Differences In Depth
• Update Function
• Planning Algorithm
• Camera Pose
• Stereo Images
• Delaunay Triangulation
• Nice Properties
• Large-scale Environments
• Ground Truth Depth
• Loop Closure
• Sparse Point Cloud
• Graph Operations
• Pose Tracking
• Bundle Adjustment
• 3D Density
• 3D Space
• Computation Time