Dorra Larnaout

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

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.

Vision-Based Differential GPS: Improving VSLAM / GPS Fusion in Urban Environment with 3D Building Models

We improve in this paper the localization accuracy of visual SLAM (VSLAM) / GPS fusion in dense urban area by using 3D building models provided by Geographic Information System (GIS). GPS inaccuracies are corrected by comparison of the reconstruction resulting from the VSLAM / GPS fusion with 3D building models. These corrected GPS data are thereafter re-injected in the fusion process. Experimental results demonstrate the accuracy improvements achieved through our proposed solution.

Fast and automatic city-scale environment modeling for an accurate 6DOF vehicle localization

To provide high quality augmented reality service in a car navigation system, accurate 6DoF localization is required. To ensure such accuracy, most of current vision-based solutions rely on an off-line large scale modeling of the environment. Nevertheless, while existing solutions require expensive equipments and/or a prohibitive computation time, we propose in this paper a complete framework that automatically builds an accurate city scale database using only a standard camera, a GPS and Geographic Information System (GIS). As illustrated in the experiments, only few minutes are required to model large scale environments. The resulting databases can then be used during a localization algorithm for high quality Augmented Reality experiences.

Fast and automatic city-scale environment modelling using hard and/or weak constrained bundle adjustments

To provide high-quality augmented reality service in a car navigation system, accurate 6 degrees of freedom (DoF) localization is required. To ensure such accuracy, most current vision-based solutions rely on an off-line large-scale modelling of the environment. Nevertheless, while existing solutions to model the environment require expensive equipments and/or a prohibitive computation time, we propose in this paper a complete framework that automatically builds an accurate large-scale database of landmarks using only a standard camera, a low-cost global positioning system (GPS) and a geographic information system (GIS). As illustrated in the experiments, only few minutes are required to model large-scale environments. The resulting databases can then be used by an on-line localization algorithm to ensure high-quality augmented reality experiences.

Driving in an augmented-city: from fast and automatic large scale environment modeling to on-line 6DOF vehicle localization

To provide high quality Augmented Reality service in a car navigation system, accurate 6DoF localization is required. To ensure such accuracy, most of current vision-based solutions rely on an off-line large scale modeling of the environment. Nevertheless, while existing solutions require expensive equipments and/or a prohibitive computation time, we propose in this paper a complete framework that automatically builds an accurate city scale database of landmarks using only a standard camera, a GPS and Geographic Information System (GIS). As illustrated in the experiments, only few minutes are required to model large scale environments. Then, a localization algorithm can use the resulting databases for a high quality Augmented Reality experiences.