Application of vision-based particle filter and visual odometry for UAV localization

Conventional UAV (abbr. Unmanned Air Vehicle) auto-pilot systems uses GPS signal for navigation. While the\nGPS signal is lost, jammed or the UAV is navigating in GPS-denied environment conventional autopilot systems\nfail to navigate safely. UAV should estimate it’s own position without the need of external signals. Localization,\nthe process of pose estimation relatively to known environment, may solve the problem of navigation without GPS\nsignal. Downward looking camera on a UAV may be used to solve pose estimation problem in combination with\nvisual odometry and other sensor data. In this paper a vision-based particle filter application is proposed to solve\nGPS-denied UAV localization. The application uses visual odometry for motion estimation, correlation coefficient\nfor apriori known map image matching with aerial imagery, KLD (abbr. Kueller-Leiblach distance) sampling for\nparticle filtering. Research using data collected during real UAV flight is performed to investigate: UAV heading\ninfluence on correlation coefficient values when matching aerial imagery with the map and measure localization\naccuracy compared to conventional GPS system and state-of-the-art odometry.