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PhD (Queensland University of Technology)
Sourav is a robotic-vision enthusiast. His research spans computer vision, robotics and deep learning, motivated by practical applications that involve a moving camera. He pioneered research in the twin challenge of visual place recognition that requires dealing with scene appearance and camera viewpoint simultaneously. His award-winning research and PhD thesis proposed novel ways of robot localization based on visual semantics, inspired by humans. He is always keen on exploring research problems related to scene understanding and robot navigation, particularly those revolving around effective representation and matching of visual information. Sourav's thesis received Executive Dean's Commendation for Outstanding Thesis Award and his research published in International Journal of Robotics Research (IJRR) won the SAGE QUT Higher Degree Research Student Publication Prize. He was deemed a Distinguished Talent by the Department of Home Affairs, Australia for his research and achievements in robotics as a future-focused technology sector. As an active researcher, he regularly publishes in top-tier conferences and journals including IJRR, RSS, ECCV, RA-L, ICRA and IROS, with majority of his research accompanying open-source code releases. He has presented his research at various international conferences across 7 different countries and has delivered invited talks at notable venues. His research has also been covered by numerous social media platforms including Brisbane Times, Engineers Australia Create Magazine, Tech Xplore and official media outlets of ARC, ACRV and QUT. Currently appointed as Postdoctoral Research Fellow with the QUT Centre for Robotics (QCR), Sourav's past research experience includes working with various government organisations, conducting collaborative research involving several educational institutes, handling various robotics setups: indoor robots for office and retail spaces, drones for delivery and autonomous cars fitted with a suite of sensors. He received his PhD from Queensland University of Technology (QUT) in 2019 while being a part of the Australian Centre for Robotic Vision (ACRV) and is currently associated with the Centre as a Research Affiliate.Additional information
- Garg, S., Suenderhauf, N. & Milford, M. (2022). Semantic-geometric visual place recognition: a new perspective for reconciling opposing views. International Journal of Robotics Research, 41(6), 573–598. https://eprints.qut.edu.au/133595
- Garg, S., Babu, M., Dharmasiri, T., Hausler, S., Suenderhauf, N., Kumar, S., Drummond, T. & Milford, M. (2019). Look no deeper: Recognizing places from opposing viewpoints under varying scene appearance using single-view depth estimation. Proceedings of the 2019 International Conference on Robotics and Automation (ICRA), 4916–4923. https://eprints.qut.edu.au/127820
- Garg, S., Suenderhauf, N. & Milford, M. (2018). Don't look back: Robustifying place categorization for viewpoint- and condition-invariant place recognition. Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), 3645–3652. https://eprints.qut.edu.au/124632
- Garg, S., Suenderhauf, N. & Milford, M. (2018). LoST? Appearance-invariant place recognition for opposite viewpoints using visual semantics. Robotics: Science and Systems XIV, 1–10. https://eprints.qut.edu.au/124630
- Talbot, B., Garg, S. & Milford, M. (2018). OpenSeqSLAM2.0: An open source toolbox for visual place recognition under changing conditions. Proceedings of the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 7758–7765.
- Garg, S., Jacobson, A., Kumar, S. & Milford, M. (2017). Improving condition- and environment-invariant place recognition with semantic place categorization. Proceedings of the 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017), 6863–6870. https://eprints.qut.edu.au/130281
- Garg, S. & Milford, M. (2017). Straightening sequence-search for appearance-invariant place recognition using robust motion estimation. Proceedings of the 2017 Australasian Conference on Robotics and Automation (ACRA 2017), 203–212. https://eprints.qut.edu.au/199347
- Skinner, J., Garg, S., Suenderhauf, N., Corke, P., Upcroft, B. & Milford, M. (2016). High-fidelity simulation for evaluating robotic vision performance. Proceedings of the 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2016), 2737–2744. https://eprints.qut.edu.au/109473