A two-view based multilayer feature graph for robot navigation

To facilitate scene understanding and robot navigation in a modern urban area, we design a multilayer feature graph (MFG) based on two views from an on-board camera. The nodes of an MFG are features such as scale invariant feature transformation (SIFT) feature points, line segments, lines, and planes while edges of the MFG represent different geometric relationships such as adjacency, parallelism, collinearity, and coplanarity. MFG also connects the features in two views and the corresponding 3D coordinate system. Building on SIFT feature points and line segments, MFG is constructed using feature fusion which incrementally, iteratively, and extensively verifies the aforementioned geometric relationships using random sample consensus (RANSAC) framework. Physical experiments show that MFG can be successfully constructed in urban area and the construction method is demonstrated to be very robust in identifying feature correspondence.

Authors

• Haifeng Li 0008
• Dezhen Song
• Yan Lu
• Jingtai Liu

Keywords

• Cameras
• Sensors
• Robot kinematics
• Buildings
• Feature extraction
• Navigation
• Robot Navigation
• Urban Areas
• Collinearity
• Coordinate System
• Line Segment
• 3D System
• Feature Points
• Corresponding Points
• Feature Fusion
• 3D Coordinates
• Geometric Relationship
• Physical Experiments
• Random Sample Consensus
• Scale-invariant Feature Transform
• Scene Understanding
• Solid Line
• End Point
• Vertical Line
• 3D Reconstruction
• Parallel Lines
• Ideal Line
• Simultaneous Localization And Mapping
• Vertical Plane
• Raw Features
• Onboard Sensors
• Camera Coordinate System
• Neighboring Regions
• Laser Ranging
• Cross-view
• Depth Camera