Zobrazeno 1 - 10
of 153
pro vyhledávání: '"de La Fortelle, Arnaud"'
While highly automated driving relies most of the time on a smooth driving assumption, the possibility of a vehicle performing harsh maneuvers with high dynamic driving to face unexpected events is very likely. The modeling of the behavior of the veh
Externí odkaz:
http://arxiv.org/abs/2306.04857
For autonomous driving or advanced driving assistance, it is key to monitor the vehicle dynamics behavior. Accurate models of this behavior include acceleration, but also the side-slip angle, that eventually results from the complex interaction betwe
Externí odkaz:
http://arxiv.org/abs/2306.04117
Accurate velocity estimation is key to vehicle control. While the literature describes how model-based and learning-based observers are able to estimate a vehicle's velocity in normal driving conditions, the challenge remains to estimate the velocity
Externí odkaz:
http://arxiv.org/abs/2303.18094
The knowledge of the states of a vehicle is a necessity to perform proper planning and control. These quantities are usually accessible through measurements. Control theory brings extremely useful methods -- observers -- to deal with quantities that
Externí odkaz:
http://arxiv.org/abs/2303.17933
Autor:
Moreau, Arthur, Piasco, Nathan, Bennehar, Moussab, Tsishkou, Dzmitry, Stanciulescu, Bogdan, de La Fortelle, Arnaud
Beyond novel view synthesis, Neural Radiance Fields are useful for applications that interact with the real world. In this paper, we use them as an implicit map of a given scene and propose a camera relocalization algorithm tailored for this represen
Externí odkaz:
http://arxiv.org/abs/2303.04869
Advanced driving functions, for assistance or full automation, require strong guarantees to be deployed. This means that such functions may not be available all the time, like now commercially available SAE Level 3 modes that are made available only
Externí odkaz:
http://arxiv.org/abs/2302.04538
We introduce a new architecture for unsupervised object-centric representation learning and multi-object detection and segmentation, which uses a translation-equivariant attention mechanism to predict the coordinates of the objects present in the sce
Externí odkaz:
http://arxiv.org/abs/2205.13271
Autor:
Moreau, Arthur, Gilles, Thomas, Piasco, Nathan, Tsishkou, Dzmitry, Stanciulescu, Bogdan, de La Fortelle, Arnaud
We propose a novel learning-based formulation for visual localization of vehicles that can operate in real-time in city-scale environments. Visual localization algorithms determine the position and orientation from which an image has been captured, u
Externí odkaz:
http://arxiv.org/abs/2205.02638
Even after decades of research, dynamic scene background reconstruction and foreground object segmentation are still considered as open problems due various challenges such as illumination changes, camera movements, or background noise caused by air
Externí odkaz:
http://arxiv.org/abs/2112.08001
Autor:
Moreau, Arthur, Piasco, Nathan, Tsishkou, Dzmitry, Stanciulescu, Bogdan, de La Fortelle, Arnaud
Neural Radiance Fields (NeRF) have recently demonstrated photo-realistic results for the task of novel view synthesis. In this paper, we propose to apply novel view synthesis to the robot relocalization problem: we demonstrate improvement of camera p
Externí odkaz:
http://arxiv.org/abs/2110.06558