Stéphane Renault

About Non-Line-Of-Sight Satellite Detection and Exclusion in a 3D Map-Aided Localization Algorithm

Reliable GPS positioning in city environment is a key issue actually, signals are prone to multipath, with poor satellite geometry in many streets. Using a 3D urban model to forecast satellite visibility in urban contexts in order to improve GPS localization is the main topic of the present article. A virtual image processing that detects and eliminates possible faulty measurements is the core of this method. This image is generated using the position estimated a priori by the navigation process itself, under road constraints. This position is then updated by measurements to line-of-sight satellites only. This closed-loop real-time processing has shown very first promising full-scale test results.

GPS/GIS localization for management of vision referenced navigation in urban environments

This communication presents localization in urban environments as a mean to construct and retrieve visual landmarks for vision based navigation. It deals with localization in a navigation map of the urban environment which is known to be inaccurate. A fuzzy sets formulation is shown to be convenient and localization is performed by computing ellipsoidal outer envelops of /spl alpha/-cuts of the pose estimation. Fusion between data from hybrid GPS and the navigation map is performed by using ellipsoidal algorithms of set membership estimation.

Collaborative Methods for Real-time Localization in Urban Centers

This article presents an effective solution for the localization of a vehicle in dense urban areas where GNSS-based methods fail because of poor satellite visibility. It advocates the use of a visual-based method processing georeferenced landmarks obtained after a learning path and stored in a new layer of the geographical information system (GIS) used for navigation. Real-time localization gives, with few failures, accurate results in the areas covered by the GIS. The integrity of the localization is obtained by running another algorithm in parallel, processing odometric data combined with the geometric model of the drivable area and, when available, GNSS data in tight coupling. An ellipsoidal confidence domain is updated by using both extended Kalman filtering (EKF) and set-membership estimation. Although less accurate, this estimation is reliable and, when the visual method fails, the availability of a confidence domain enables us to speed up the restart of the visual method while navigating cautiously. A large-scale experiment (>4 km) was conducted in the centre of Paris. We compare the absolute localization results with the ground truth obtained by combining RTK-GPS and a high-end inertial measurement unit (IMU).