Ioan Andrei Bârsan
I'm a graduate student at the University of Toronto doing research on combining classic geometric methods with learning for computer vision. My supervisor is Prof. Raquel Urtasun. At the same time, I am working full-time at Uber ATG Toronto (also with Raquel) to apply my research to the real-world challenges of self-driving cars. Before coming to UofT, I did my Master's at ETH Zurich, writing my Master's Thesis on large-scale dense mapping under Prof. Andreas Geiger's supervision. I am originally from Brașov, Romania.
Research Papers
Learning to Localize through Compressed Binary Maps
X. Wei, I. A. Bârsan, J. Martinez, S. Wang, 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
