5 accepted papers
Detecting road features is a key enabler for autonomous driving and localization. For instance, a reliable detection of poles which are widespread in road environments can improve localization. Modern deep learning-based perception systems need a significant amount of annotated data. Automatic annot…
In urban environments where GNSS performance is degraded, localization can be performed using stable and geo-referenced map features detected by on-board sensors. Prior maps are prone to errors which have a direct impact on localization accuracy. By exchanging observed features and sharing their map…
Maps provide an important source of information for autonomous vehicles. They can be used along with cameras and lidars to localize the vehicle. This requires the ability to correctly associate observations to features referenced in the map. The problem is all the more difficult that all observation
Localization is one of the main challenges to be addressed to develop autonomous vehicles able to perform complex maneuvers on roads opened to public traffic. Having an accurate dead-reckoning system is an essential step to reach this objective. This paper presents a dead-reckoning model for car-lik…
Satellite positioning is a key technology for autonomous navigation in outdoors environments. When using standalone computation with mono-frequency receivers, positioning errors are not in accordance with the required performance. Nevertheless, since errors are strongly time-correlated, a GPS fix is…