Dr Sourav Garg
Grant-Funded Researcher (B)
Australian Institute for Machine Learning - Projects
Faculty of Sciences, Engineering and Technology
Eligible to supervise Masters and PhD (as Co-Supervisor) - email supervisor to discuss availability.
Sourav works at the intersection of robotic vision and machine learning, enabling robots to see, understand and act in their environment as an intelligent embodied agent. https://oravus.github.io/
He completed his Phd at QUT, Australia, with pioneering research in the twin challenge of Visual Place Recognition that requires dealing with variations in 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.
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 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 RA-L, ICRA, IROS, CoRL, CVPR, ICCV, ECCV, and ICLR. His research has also been covered by numerous social media platforms including Brisbane Times, Engineers Australia Create Magazine, Tech Xplore and ARC.
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Appointments
Date Position Institution name 2023 - ongoing Research Fellow University of Adelaide 2019 - 2023 Research Fellow Queensland University of Technology -
Language Competencies
Language Competency English Can read, write, speak, understand spoken and peer review Hindi Can read, write, speak, understand spoken and peer review Panjabi; Punjabi Can read, write, speak, understand spoken and peer review -
Education
Date Institution name Country Title 2015 - 2019 Queensland University of Technology Australia PhD 2008 - 2012 Thapar University India Bachelor of Engineering -
Research Interests
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Journals
Year Citation 2023 Schubert, S., Neubert, P., Garg, S., Milford, M., & Fischer, T. (2023). Visual Place Recognition: A Tutorial. IEEE Robotics & Automation Magazine, 31(3), 2-16.
Scopus72023 Hausler, S., Garg, S., Chakravarty, P., Shrivastava, S., Vora, A., & Milford, M. (2023). Locking On: Leveraging Dynamic Vehicle-Imposed Motion Constraints to
Improve Visual Localization.2023 Keetha, N., Mishra, A., Karhade, J., Jatavallabhula, K. M., Scherer, S., Krishna, M., & Garg, S. (2023). AnyLoc:Towards Universal Visual Place Recognition. IEEE Robotics and Automation Letters, 9(2), 1286-1293.
Scopus92023 Keetha, N., Mishra, A., Karhade, J., Jatavallabhula, K. M., Scherer, S., Krishna, M., & Garg, S. (2023). AnyLoc: Towards Universal Visual Place Recognition. 2022 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.
Scopus39 WoS372022 Hausler, S., Xu, M., Garg, S., Chakravarty, P., Shrivastava, S., Vora, A., & Milford, M. (2022). Improving Worst Case Visual Localization Coverage via Place-Specific Sub-Selection in Multi-Camera Systems. IEEE Robotics and Automation Letters, 7(4), 10112-10119.
Scopus6 WoS52022 Malone, C., Garg, S., Xu, M., Peynot, T., & Milford, M. (2022). Improving Road Segmentation in Challenging Domains Using Similar Place Priors. IEEE Robotics and Automation Letters, 7(2), 3555-3562.
Scopus1 WoS12022 Khaliq, A., Milford, M., & Garg, S. (2022). MultiRes-NetVLAD: Augmenting Place Recognition Training with Low-Resolution Imagery. IEEE Robotics and Automation Letters, 7(2), 3882-3889.
Scopus25 WoS62021 Keetha, N. V., Milford, M., & Garg, S. (2021). A Hierarchical Dual Model of Environment- And Place-Specific Utility for Visual Place Recognition. IEEE Robotics and Automation Letters, 6(4), 6969-6976.
Scopus21 WoS92021 Zaffar, M., Garg, S., Milford, M., Kooij, J., Flynn, D., McDonald-Maier, K., & Ehsan, S. (2021). VPR-Bench: An Open-Source Visual Place Recognition Evaluation Framework with Quantifiable Viewpoint and Appearance Change. International Journal of Computer Vision, 129(7), 2136-2174.
Scopus75 WoS312021 Garg, S., & Milford, M. (2021). SeqNet: Learning Descriptors for Sequence-Based Hierarchical Place Recognition. IEEE Robotics and Automation Letters, 6(3), 4305-4312.
Scopus61 WoS292021 Garg, S., & Milford, M. (2021). SeqNetVLAD vs PointNetVLAD: Image Sequence vs 3D Point Clouds for
Day-Night Place Recognition.2021 Lengyel, A., Garg, S., Milford, M., & Gemert, J. C. V. (2021). Zero-Shot Day-Night Domain Adaptation with a Physics Prior. Proceedings of the IEEE/CVF International Conference on Computer
Vision (ICCV), 2021, pp. 4399-4409.2020 Garg, S., Harwood, B., Anand, G., & Milford, M. (2020). Delta Descriptors: Change-Based Place Representation for Robust Visual Localization. IEEE Robotics and Automation Letters, 5(4), 5120-5127.
Scopus31 WoS15 -
Books
Year Citation 2020 Garg, S., Sünderhauf, N., Dayoub, F., Morrison, D., Cosgun, A., Carneiro, G., . . . Milford, M. (2020). Semantics for Robotic Mapping, Perception and Interaction: A Survey (Vol. 8). United States: Now Publishers.
DOI -
Conference Papers
Year Citation 2024 Garg, S., Rana, K., Hosseinzadeh, M., Mares, L., Sünderhauf, N., Dayoub, F., & Reid, I. (2024). RoboHop: Segment-based Topological Map Representation for Open-World Visual Navigation. In Proceedings - IEEE International Conference on Robotics and Automation Vol. 35 (pp. 4090-4097). Yokohama, Japan: IEEE.
DOI2023 Xu, M., Garg, S., Milford, M., & Gould, S. (2023). Deep Declarative Dynamic Time Warping for End-to-End Learning of
Alignment Paths.2023 Vankadari, M., Golodetz, S., Garg, S., Shin, S., Markham, A., & Trigoni, N. (2023). When the Sun Goes Down: Repairing Photometric Losses for All-Day Depth Estimation. In Proceedings of Machine Learning Research Vol. 205 (pp. 1992-2003). Auckland, New Zealand: MLR Press.
Scopus32023 Kalwar, S., Patel, D., Aanegola, A., Konda, K. R., Garg, S., & Krishna, K. M. (2023). GDIP: Gated Differentiable Image Processing for Object Detection in Adverse Conditions. In Proceedings - IEEE International Conference on Robotics and Automation Vol. 2023-May (pp. 7083-7089). London, United Kingdom: IEEE.
DOI Scopus142023 Hausler, S., Garg, S., Chakravarty, P., Shrivastava, S., Vora, A., & Milford, M. (2023). Locking On: Leveraging Dynamic Vehicle-Imposed Motion Constraints to Improve Visual Localization. In IEEE International Conference on Intelligent Robots and Systems Vol. 31 (pp. 5258-5265). Online: IEEE.
DOI2023 Hausler, S., Garg, S., Chakravarty, P., Shrivastava, S., Vora, A., & Milford, M. (2023). DisPlacing Objects: Improving Dynamic Vehicle Detection via Visual Place Recognition under Adverse Conditions. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 1373-1380). Online: IEEE.
DOI Scopus22023 Rana, K., Haviland, J., Garg, S., Abou-Chakra, J., Reid, I., & Sünderhauf, N. (2023). SayPlan: Grounding Large Language Models using 3D Scene Graphs for Scalable Robot Task Planning. In Proceedings of Machine Learning Research Vol. 229 (pp. 50 pages). Atlanta, Georgia, USA: Maching Learning Research Press.
Scopus32023 Sharma, A., Mehan, Y., Dasu, P., Garg, S., & Krishna, K. M. (2023). Hierarchical Unsupervised Topological SLAM. In IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC Vol. abs/1902.09516 (pp. 4623-4628). Online: IEEE.
DOI2023 Xu, M., Garg, S., Milford, M., & Gould, S. (2023). DEEP DECLARATIVE DYNAMIC TIME WARPING FOR END-TO-END LEARNING OF ALIGNMENT PATHS. In 11th International Conference on Learning Representations, ICLR 2023.
Scopus22021 Hausler, S., Garg, S., Xu, M., Milford, M., & Fischer, T. (2021). Patch-NetVlad: Multi-scale fusion of locally-global descriptors for place recognition. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 14136-14147). Nashville, TN, USA: IEEE.
DOI Scopus227 WoS822021 Garg, S., Fischer, T., & Milford, M. (2021). Where Is Your Place, Visual Place Recognition?. In Z. -H. Zhou (Ed.), IJCAI International Joint Conference on Artificial Intelligence (pp. 4416-4425). Montreal-themed virtual reality: International Joint Conferences on Artificial Intelligence Organization.
DOI Scopus422021 Malone, C., Garg, S., Peynot, T., & Milford, M. (2021). Improving Semantic Segmentation with Calibrated Whole Image and Patch-Based Similar Place Priors. In Australasian Conference on Robotics and Automation, ACRA Vol. 2021-December. Online: Australasian Robotics and Automation Association. 2021 Singh Parihar, U., Gujarathi, A., Mehta, K., Tourani, S., Garg, S., Milford, M., & Krishna, K. M. (2021). RoRD: Rotation-Robust Descriptors and Orthographic Views for Local Feature Matching. In IEEE International Conference on Intelligent Robots and Systems Vol. abs 1812 11941 (pp. 1593-1600). Online: IEEE.
DOI Scopus24 WoS52021 Tourani, S., Desai, D., Parihar, U. S., Garg, S., Sarvadevabhatla, R. K., Milford, M., & Krishna, K. M. (2021). Early bird: Loop closures from opposing viewpoints for perceptually-aliased indoor environments. In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. VISAPP Vol. 5 (pp. 409-416). Vienna, Austria: SCITEPRESS - Science and Technology Publications.
DOI Scopus12021 Garg, S., Vankadari, M., & Milford, M. (2021). SeqMatchNet: Contrastive Learning with Sequence Matching for Place Recognition & Relocalization. In A. Faust, D. Hsu, & G. Neumann (Eds.), Proceedings of the 5th Conference on Robot Learning Vol. 164 (pp. 429-443). London, UK: MLR Press.
Scopus172020 Garg, S., & Milford, M. (2020). Fast, Compact and Highly Scalable Visual Place Recognition through Sequence-based Matching of Overloaded Representations. In Proceedings - IEEE International Conference on Robotics and Automation Vol. 159 (pp. 3341-3348). Paris, France: IEEE.
DOI Scopus20 WoS122020 Vankadari, M., Garg, S., Majumder, A., Kumar, S., & Behera, A. (2020). Unsupervised Monocular Depth Estimation for Night-Time Images Using Adversarial Domain Feature Adaptation. In A. Vedaldi, H. Bischof, T. Brox, & J. -M. Frahm (Eds.), Computer Vision - ECCV 2020 Vol. 12373 LNCS (pp. 443-459). Glasgow, UK: Springer International Publishing.
DOI Scopus312019 Garg, S., Babu, M. V., Dharmasiri, T., Hausler, S., Suenderhauf, N., Kumar, S., . . . Milford, M. (2019). Look no deeper: Recognizing places from opposing viewpoints under varying scene appearance using single-view depth estimation. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2019) Vol. 2019-May (pp. 4916-4923). Piscataway, NJ, USA: IEEE.
DOI Scopus18 WoS122019 Talbot, B., Garg, S., & Milford, M. (2019). OpenSeqSLAM2.0: An Open Source Toolbox for Visual Place Recognition under Changing Conditions. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018) Vol. 33 (pp. 7758-7765). New York City, NY, USA: IEEE.
DOI Scopus22 WoS172018 Garg, S., Suenderhauf, N., & Milford, M. (2018). Don't look back: Robustifying place categorization for viewpoint- and condition-invariant place recognition. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2018) (pp. 3645-3652). Piscataway, NJ.: IEEE.
DOI Scopus57 WoS362018 Garg, S., Suenderhauf, N., & Milford, M. (2018). LoST? Appearance-Invariant Place Recognition for Opposite Viewpoints using Visual Semantics. In Proceedings of Robotics: Science and Systems XIV (RSS 2018) Vol. 2018 (pp. 1-10). California, USA: Robotics: Science and Systems Foundation.
DOI Scopus72 WoS302017 Garg, S., Jacobson, A., Kumar, S., & Milford, M. (2017). Improving condition- and environment-invariant place recognition with semantic place categorization. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017) Vol. 2017-September (pp. 6863-6870). Vancouver Convention Centre, Vancouver, British Columbia, Canada: IEEE.
DOI Scopus24 WoS152017 Garg, S., & Milford, M. (2017). Straightening sequence-search for appearance-invariant place recognition using robust motion estimation. In Australasian Conference on Robotics and Automation, ACRA Vol. 2017-December (pp. 203-212). Sydney, Australia: Australian Robotics & Automation Association.
Scopus32016 Skinner, J., Garg, S., Sünderhauf, N., Corke, P., Upcroft, B., & Milford, M. (2016). High-fidelity simulation for evaluating robotic vision performance. In IEEE International Conference on Intelligent Robots and Systems Vol. 2016-November (pp. 2737-2744). Daejeon, SOUTH KOREA: IEEE.
DOI Scopus19 WoS132015 Garg, S., Kumar, S., Ratnakaram, R., & Guha, P. (2015). An occlusion reasoning scheme for monocular pedestrian tracking in dynamic scenes. In AVSS 2015 - 12th IEEE International Conference on Advanced Video and Signal Based Surveillance Vol. 18 (pp. 6 pages). IOSB, Karlsruhe Inst Technol & Fraunhofer, Karlsruhe, GERMANY: IEEE.
DOI Scopus12015 Garg, S., Hassan, E., Kumar, S., & Guha, P. (2015). A hierarchical frame-by-frame association method based on graph matching for multi-object tracking. In G. Bebis, R. Boyle, B. Parvin, D. Koracin, I. Pavlidis, R. Feris, . . . G. Weber (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 9474 (pp. 138-150). Las Vegas, NV: SPRINGER INTERNATIONAL PUBLISHING AG.
DOI Scopus12015 Kejriwal, N., Garg, S., & Kumar, S. (2015). Product counting using images with application to robot-based retail stock assessment. In IEEE Conference on Technologies for Practical Robot Applications, TePRA Vol. 2015-August (pp. 6 pages). Woburn, MA: IEEE.
DOI Scopus32 WoS32014 Gupta, M., Kumar, S., Garg, S., Kejriwal, N., & Behera, L. (2014). A novel SURF-based algorithm for tracking a 'Human' in a dynamic environment. In 2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014 Vol. 77 (pp. 1004-1009). Singapore, SINGAPORE: IEEE.
DOI Scopus2 WoS22014 Kumar, S., Garg, S., & Kejriwal, N. (2014). A tea-serving robot for office environment. In Proceedings for the Joint Conference of ISR 2014 - 45th International Symposium on Robotics and Robotik 2014 - 8th German Conference on Robotics, ISR/ROBOTIK 2014 (pp. 29-34). 2013 Gupta, M., Garg, S., Kumar, S., & Behera, L. (2013). An on-line visual human tracking algorithm using SURF-based dynamic object model. In 2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings Vol. 74 (pp. 3875-3879). Melbourne, AUSTRALIA: IEEE.
DOI Scopus15 WoS10 -
Theses
Year Citation - Garg, S. (n.d.). Robust Visual Place Recognition under Simultaneous Variations in Viewpoint and Appearance. -
Preprint
Year Citation 2024 Mehan, Y., Gupta, K., Jayanti, R., Govil, A., Garg, S., & Krishna, M. (2024). QueSTMaps: Queryable Semantic Topological Maps for 3D Scene
Understanding.2024 Garg, S., Rana, K., Hosseinzadeh, M., Mares, L., Sünderhauf, N., Dayoub, F., & Reid, I. (2024). RoboHop: Segment-based Topological Map Representation for Open-World
Visual Navigation.2024 Garg, K., Puligilla, S. S., Kolathaya, S., Krishna, M., & Garg, S. (2024). Revisit Anything: Visual Place Recognition via Image Segment Retrieval. 2023 Hausler, S., Garg, S., Chakravarty, P., Shrivastava, S., Vora, A., & Milford, M. (2023). DisPlacing Objects: Improving Dynamic Vehicle Detection via Visual Place
Recognition under Adverse Conditions.
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Current Higher Degree by Research Supervision (University of Adelaide)
Date Role Research Topic Program Degree Type Student Load Student Name 2023 Co-Supervisor 3D Scene Understanding and Change Tracking Doctor of Philosophy Doctorate Full Time Mr Chun-Jung Lin
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