Ioan Andrei Bârsan
Research Papers
Learning to Localize through Compressed Binary Maps
X. Wei, I. A. Bârsan, S. Wang, J. Martinez, R. Urtasun
One of the main difficulties of scaling current localization systems to large environments is the on-board storage required for the maps. In this paper we propose to learn to compress the map representation such that it is optimal for the localization task. [...] [PDF]
Conference on Computer Vision and Pattern Recognition (CVPR), 2019
One of the main difficulties of scaling current localization systems to large environments is the on-board storage required for the maps. In this paper we propose to learn to compress the map representation such that it is optimal for the localization task. [...] [PDF]
Conference on Computer Vision and Pattern Recognition (CVPR), 2019
Learning to Localize Using a LiDAR Intensity Map
I. Bârsan, S. Wang, A. Pokrovsky, R. Urtasun
In this paper we propose a real-time, calibration-agnostic and effective localization system for self-driving cars. Our method learns to embed the online LiDAR sweeps and intensity map into a joint deep embedding space. [...] [PDF]
Conference on Robot Learning (CORL), 2018
In this paper we propose a real-time, calibration-agnostic and effective localization system for self-driving cars. Our method learns to embed the online LiDAR sweeps and intensity map into a joint deep embedding space. [...] [PDF]
Conference on Robot Learning (CORL), 2018
Robust Dense Mapping for Large-Scale Dynamic Environments
I. Bârsan, P. Liu, M. Pollefeys, A. Geiger
We present a stereo-based dense mapping algorithm for large-scale dynamic urban environments. In contrast to other existing methods, we simultaneously reconstruct the static background, the moving objects, and the potentially moving but currently stationary objects separately, which is desirable for high-level mobile robotic tasks such as path planning in crowded environments. [...] [PDF]
Video: [LINK]
Project Page: [LINK]
International Conference on Robotics and Automation (ICRA), 2018
We present a stereo-based dense mapping algorithm for large-scale dynamic urban environments. In contrast to other existing methods, we simultaneously reconstruct the static background, the moving objects, and the potentially moving but currently stationary objects separately, which is desirable for high-level mobile robotic tasks such as path planning in crowded environments. [...] [PDF]
Video: [LINK]
Project Page: [LINK]
International Conference on Robotics and Automation (ICRA), 2018