Vehicle 6-DoF localization based on SLAM constrained by GPS and digital elevation model information

Dorra Larnaout (1) , Vincent Gay-Bellile (1) , Steve Bourgeois (1) , Michel Dhome (2)

1 LVIC - Laboratoire Vision et Ingénierie des Contenus
2 IP - Institut Pascal

Vehicle geo-localization based on monocular visual Simultaneous Localization And Mapping (SLAM) remains a challenging issue mainly due to the accumulation errors and scale factor drift. To tackle these limitations, a common solution is to introduce geo-referenced information into the visual SLAM algorithm. In this paper, we propose two different bundle adjustment processes that merge both GPS measurements and “Digital Elevation Model” (DEM) data. Proposed solutions are devoted to ensure an accurate and robust geo-localization in both rural and urban environment. Experiments on synthetic and large scale real sequences show that, in addition to the real-time (i.e. about 30 Hz) performances, we obtain an accurate 6DoF localization.

Keywords: SLAM Simultaneous Localisation and Mapping; Digital Elevation Model; GPS; Local bundle adjustment

Domains: Engineering Sciences [physics]; Signal and Image processing

Dorra Larnaout, Vincent Gay-Bellile, Steve Bourgeois, Michel Dhome. Vehicle 6-DoF localization based on SLAM constrained by GPS and digital elevation model information. IEEE International Conference on Image Processing (ICIP), Sep 2013, Melbourne, Australia. ⟨10.1109/ICIP.2013.6738516⟩. ⟨cea-01830492⟩