I’m a PhD student in the Machine Learning Group at the University of Toronto, focusing on computer vision and deep learning for autonomous robotics. I started in September 2017, under the supervision of Professor Raquel Urtasun.

                                  In addition to this, I am also a full-time researcher at Uber Advanced Technologies Group (ATG) Toronto, also led by Professor Urtasun, working on applying my research to the challenges associated with autonomous driving in the real world.

                                  In addition to machine learning and computer vision, my research interests include robotics and long-term autonomy. I am also interested in machine learning security, and I believe that more research is needed in this area (together with its complementary subfield, interpretability), given the growing influence of various machine learning-powered technologies on our daily lives.




                                  express科学加速器ios,  Sasha Doubov,  Jack Fan, Ioan Andrei Bârsan,  Shenlong Wang,  Gellért Máttyus and Raquel Urtasun
                                  International Conference on Intelligent Robots and Systems (sgreen科学加速器) 2020
                                  Web (Coming Soon!) PDF (Coming Soon!) BibTeX Play with it! (Coming Soon!)

                                  TL;DR: A new self-driving dataset containing >30M HD images and LiDAR sweeps covering Pittsburgh over one year, all with centimeter-level pose accuracy. We investigate the potential of retrieval-based localization in this setting, and show that simple architecture (e.g., ResNet + global pool) perform surprisingly well, outperforming more complex architectures like NetVLAD.

                                  The figure shows the geographic (top) and temporal (bottom, x = date, y = time of day) extent of the data.

                                  We are hard at work preparing the benchmark website and data download! Stay tuned!


                                  Wei-Chiu Ma*,  Ignacio Tartavull*Ioan Andrei Bârsan*,  Shenlong Wang*,  Min Bai,  Gellért Máttyus,  Namdar Homayounfar,  Shrinidhi Kowshika Lakshmikanth,  Andrei Pokrovsky and Raquel Urtasun
                                  International Conference on Intelligent Robots and Systems (就爱加速安卓版) 2019
                                  Note: *denotes equal contribution.
                                  PDF (arXiv) BibTeX Talk Slides (PDF) Talk Slides (Apple Keynote)

                                  TL;DR: We use very sparse maps consisting in lane graphs (i.e., polylines) and stored traffic sign positions to localize autonomous vehicles. These maps take up ~0.5MiB/km2, compared to, e.g., LiDAR ground intensity images which can take >100MiB/km2. We use these maps in the context of a histogram filter localizer, and show median lateral accuracy of 0.05m and median longitudinal accuracy of 1.12m on a highway dataset.

                                  Learning to Localize through Compressed Binary Maps (CVPR 2019)

                                  Sgreen加速器官网*Ioan Andrei Bârsan*,  Shenlong Wang*,  Julieta Martinez and Raquel Urtasun
                                  International Conference on Computer Vision and Pattern Recognition (CVPR) 2019
                                  Note: *denotes equal contribution.
                                  PDF BibTeX Poster Video

                                  TL;DR: High-resolution maps can take up a lot of storage. We use neural networks to perform task-specific compression to address this issue by learning a special-purpose compression scheme specifically for localization. We achieve two orders of magnitude of improvement over traditional methods like WebP, as well as less than half the bitrate of a general-purpose learning-based compression scheme. For reference, PNG takes up 700× more storage on our dataset.

                                  Learning to Localize Using a LiDAR Intensity Map (CoRL 2018)

                                  Localizer preview image
                                  Ioan Andrei Bârsan*,  Shenlong Wang*,  Andrei Pokrovsky and Raquel Urtasun Proceedings of the Second Conference on Robot Learning (CoRL) 2018
                                  sgreen安卓安装包 *denotes equal contribution.
                                  PDF BibTeX Poster Talk Slides (PDF) Video

                                  TL;DR: Matching-based localization methods using LiDAR can provide centimeter-level accuracy, but require careful beam intensity calibration in order to perform well. In this paper, we cast the matching problem as a learning task and show that it is possible to learn to match online LiDAR observations to a known map without calibrated intensities.

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                                  express科学加速器ios,  sgreen安卓安装包,  Marc Pollefeys and Sgreen加速器官网
                                  Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2018
                                  Web PDF BibTeX Poster Code

                                  TL;DR: A system for outdoor online mapping using a stereo camera capable of also reconstructing the dynamic objects it encounters, in addition to the static map. Supports map pruning to eliminate stereo artifacts and reduce memory consumption to less than half.







                                  Before starting my PhD, I completed my Master’s in Computer Science at ETH Zurich. For my Master’s Thesis, I developed DynSLAM, a dense mapping system capable of simultaneously reconstructing dynamic and potentially dynamic objects encountered in an environment, in addition to the background map, using just stereo input. More details can be found on the sgreen科学加速器.

                                  Previously, while doing my undergraduate studies at Transilvania University, in Brașov, Romania, I interned at Microsoft (2013, Redmond, WA), Google (2014, New York, NY) and Twitter (2015, San Francisco, CA), working on projects related to privacy, data protection, and data pipeline engineering.

                                  I am originally from Brașov, Romania, a lovely little town which I encourage everybody to visit, together with the rest of Southeast Europe.


                                  Email me at iab (at) cs (dawt) toronto (dawt) edu.

                                  Find me on Twitter, GitHub, Google Scholar, LinkedIn, or green加速器官网下载.

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