Topometric localization on a road network

Current GPS-based devices have difficulty localizing in cases where the GPS signal is unavailable or insufficiently accurate. This paper presents an algorithm for localizing a vehicle on an arbitrary road network using vision, road curvature estimates, or a combination of both. The method uses an extension of topometric localization, which is a hybrid between topological and metric localization. The extension enables localization on a network of roads rather than just a single, non-branching route. The algorithm, which does not rely on GPS, is able to localize reliably in situations where GPS-based devices fail, including “urban canyons” in downtown areas and along ambiguous routes with parallel roads. We demonstrate the algorithm experimentally on several road networks in urban, suburban, and highway scenarios. We also evaluate the road curvature descriptor and show that it is effective when imagery is sparsely available.

Authors

• Danfei Xu
• Hernán Badino
• Daniel F. Huber

Keywords

• Roads
• Vehicles
• Visualization
• Databases
• Probability density function
• Global Positioning System
• Measurement
• Road Network
• GPS Signals
• Urban Canyon
• Light Conditions
• Average Error
• Localization Error
• Navigation System
• Particle Filter
• Maximum A Posteriori
• Curve Measurements
• Road Segments
• Real-time Position
• Simultaneous Localization And Mapping
• Visual Localization
• State Transition Probability
• Network Routing
• Split Point
• Loop Closure
• Visual Odometry
• Merging Point
• Google Street View
• Priority Map
• Sparse Imaging
• Directed Graph
• Vehicle Position
• Road Geometry
• Kalman Filter
• Entire Route
• Bayesian Filtering