Geo-referencing for UAV navigation using environmental classification

A UAV navigation system relying on GPS is vulnerable to signal failure, making a drift free backup system necessary. We introduce a vision based geo-referencing system that uses pre-existing maps to reduce the long term drift. The system classifies an image according to its environmental content and thereafter matches it to an environmentally classified map over the operational area. This map matching provides a measurement of the absolute location of the UAV, that can easily be incorporated into a sensor fusion framework. Experiments show that the geo-referencing system reduces the long term drift in UAV navigation, enhancing the ability of the UAV to navigate accurately over large areas without the use of GPS.

Authors

• Fredrik Lindsten
• Jonas Callmer
• Henrik Ohlsson
• David Törnqvist
• Thomas B. Schön
• Fredrik Gustafsson

Keywords

• Unmanned aerial vehicles
• Global Positioning System
• Satellite navigation systems
• Position measurement
• Noise measurement
• Simultaneous localization and mapping
• Sensor fusion
• Cameras
• Table lookup
• Coordinate measuring machines
• UAV Navigation
• Matching Model
• Absolute Position
• Measurement Model
• Probability Density Function
• Image Segmentation
• Angular Velocity
• Lookup Table
• Segmentation Algorithm
• Horizontal Position
• Inertial Measurement Unit
• Pose Estimation
• Reference Map
• Circular Region
• Template Matching
• Pixel Coordinates
• Measurement Equation
• Instability Issues
• Normalized Cross-correlation
• Classification Uncertainty
• Probabilistic Classification
• Onboard Camera
• Neural Network
• Class Probabilities