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Dr Peyman Moghadam

Adjunct Associate Professor

School of Electrical Engineering and Computer Science
Faculty of Engineering, Architecture and Information Technology
p.moghadam@uq.edu.au

Overview

Peyman Moghadam is an Adjunct Associate Professor at the University of Queensland (UQ). He is a Principal Research Scientist at CSIRO Data61 as well as Professor (Adjunct) at the Queensland University of Technology (QUT). He leads the Embodied AI Research Cluster at CSIRO Data61, working at the intersection of Robotics and Machine learning. He is also the Spatiotemporal AI portfolio Leader at the CSIRO's Machine Learning and Artificial Intelligence (MLAI) Future Science Platform and oversees research and development of MLAI methods for scientific discovery in spatiotemporal data streams. In 2022, he served as a Visiting Professor at ETH Zürich. In 2019, he held a Visiting Scientist appointment at the University of Bonn. Peyman has led several large-scale multidisciplinary projects and won numerous awards, including CSIRO's Julius Career Award, National, and Queensland state iAward for Research and Development, CSIRO’s Collaboration Medal and the Lord Mayor’s Budding Entrepreneurs Award. His current research interests include self-supervised learning for robotics, embodied AI, 3D multi-modal perception (3D++), robotics, and computer vision.

Research Interests

  • Embodied Intelligence; Self-Supervised Learning; spatiotemporal learning
  • Robotics, Computer Vision, Machine Learning, Deep Learning
  • Beyond visible Spectrum Perception (Hyperspectral, Thermal)
  • 3D LiDAR SLAM; 3D Scene understanding; 3D Segmentation

Publications

View all Publications

Supervision

  • Generalizing Implicit Representations for Robotics Manipulation of Articulated Objects

    Doctor Philosophy

View all Supervision

Available Projects

  • Self-Supervised Learning for 3D Multimodal Perception

    Potential impact of deep learning is limited due to the lack of large, annotated, and high-quality datasets in domains of interest. Annotating such datasets is laborious, costly and time-consuming. This project proposes to develop self-supervised learning systems to extract and use the relevant context given by strong prior spatio-temporal models (e.g. dense 3D reconstructions) as supervisory signals in training. This new concept will investigate model structures that encodes spatio-temporal data, and show rapid adaptation of models to new domains (few-shot learning) using trained embeddings layers (self-supervised, or prior data).

  • 3D Scene Understanding

    Simultaneous Localization and Mapping (SLAM) is a key enabling component of driverless vehicles, robotics and augmented reality. The SLAM goal is to estimate pose of the vehicle and simultaneously generate dense 3D scene reconstruction. At CSIRO we have developed and deployed state-of-the-art 3D LiDAR-based SLAM systems for the past decade. There is a new direction of research at the intersection of deep learning and geometry-based 3D SLAM. The research in this PhD programme will develop algorithms for geometry-based Deep Learning SLAM in a dynamic and unstructured environment. The PhD programme will involve the development of self or semi-supervised learning methods to address the significant weakness of most current deep networks.

  • Hyperspectral Deep Learning

    Hyperspectral cameras are currently undergoing a change from bulky and expensive equipment towards mobile and portable devices. A hyperspectral camera comprises of hundreds of bands with shortwave dependencies. Compared to conventional colour cameras (RGB bands), one could use these shortwave dependencies to design and develop a deep network for object classification, semantic segmentation and scene understanding. Both spectral and spatial relationship needs to be modelled by the deep networks simultaneously. The research in this PhD programme will develop algorithms for hyperspectral deep learning. The PhD programme will involve the development of learning with self-supervision algorithms to address the significant weakness of most current deep networks.

View all Available Projects

Publications

Book Chapter

Journal Article

Conference Publication

  • Deep robust multi-robot re-localisation in natural environments

    Ramezani, Milad, Griffiths, Ethan, Haghighat, Maryam, Pitt, Alex and Moghadam, Peyman (2023). Deep robust multi-robot re-localisation in natural environments. 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Detroit, MI United States, 1 - 5 October 2023. Piscataway, NJ United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/iros55552.2023.10341798

  • Uncertainty-Aware Lidar Place Recognition in Novel Environments

    Mason, Keita, Knights, Joshua, Ramezani, Milad, Moghadam, Peyman and Miller, Dimity (2023). Uncertainty-Aware Lidar Place Recognition in Novel Environments. IEEE. doi: 10.1109/iros55552.2023.10341383

  • Measuring Situational Awareness Latency in Human-Robot Teaming Experiments

    Senaratne, Hashini, Pitt, Alex, Talbot, Fletcher, Moghadam, Peyman, Sikka, Pavan, Howard, David, Williams, Jason, Kulić, Dana and Paris, Cécile (2023). Measuring Situational Awareness Latency in Human-Robot Teaming Experiments. 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), Busan South Korea, Aug 28-31, 2023. NEW YORK: IEEE. doi: 10.1109/ro-man57019.2023.10309377

  • Learning Partial Correlation based Deep Visual Representation for Image Classification

    Rahman, Saimunur, Koniusz, Piotr, Wang, Lei, Zhou, Luping, Moghadam, Peyman and Sun, Changming (2023). Learning Partial Correlation based Deep Visual Representation for Image Classification. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver Canada, Jun 17-24, 2023. LOS ALAMITOS: IEEE. doi: 10.1109/cvpr52729.2023.00603

  • Wild-places: a large-scale dataset for lidar place recognition in unstructured natural environments

    Knights, Joshua, Vidanapathirana, Kavisha, Ramezani, Milad, Sridharan, Sridha, Fookes, Clinton and Moghadam, Peyman (2023). Wild-places: a large-scale dataset for lidar place recognition in unstructured natural environments. 2023 IEEE International Conference on Robotics and Automation (ICRA), London, United Kingdom, 29 May - 2 June 2023. Washington, DC, United States: IEEE Computer Society. doi: 10.1109/icra48891.2023.10160432

  • Flashback for Continual Learning

    Mahmoodi, Leila, Harandi, Mehrtash and Moghadam, Peyman (2023). Flashback for Continual Learning. IEEE/CVF International Conference on Computer Vision (ICCV), Paris France, Oct 02-06, 2023. LOS ALAMITOS: IEEE COMPUTER SOC. doi: 10.1109/ICCVW60793.2023.00368

  • InCloud: incremental learning for point cloud place recognition

    Knights, Joshua, Moghadam, Peyman, Ramezani, Milad, Sridharan, Sridha and Fookes, Clinton (2022). InCloud: incremental learning for point cloud place recognition. 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Kyoto, Japan, 23-27 October 2022. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/iros47612.2022.9981252

  • A real-time edge-AI system for reef surveys

    Li, Yang, Liu, Jiajun, Kusy, Brano, Marchant, Ross, Do, Brendan, Merz, Torsten, Crosswell, Joey, Steven, Andy, Tychsen-Smith, Lachlan, Ahmedt-Aristizabal, David, Oorloff, Jeremy, Moghadam, Peyman, Babcock, Russ, Malpani, Megha and Oerlemans, Ard (2022). A real-time edge-AI system for reef surveys. ACM MobiCom '22: The 28th Annual International Conference on Mobile Computing and Networking, Sydney, NSW Australia, 17-21 October 2022. New York, NY, USA: ACM. doi: 10.1145/3495243.3558278

  • LoGG3D-Net: Locally Guided Global Descriptor Learning for 3D Place Recognition

    Vidanapathirana, Kavisha, Ramezani, Milad, Moghadam, Peyman, Sridharan, Sridha and Fookes, Clinton (2022). LoGG3D-Net: Locally Guided Global Descriptor Learning for 3D Place Recognition. 2022 International Conference on Robotics and Automation (ICRA), Philadelphia, PA United States, 23-27 May 2022. Piscataway, NJ United States: IEEE. doi: 10.1109/icra46639.2022.9811753

  • Why object detectors fail: investigating the influence of the dataset

    Miller, Dimity, Goode, Georgia, Bennie, Callum, Moghadam, Peyman and Jurdak, Raja (2022). Why object detectors fail: investigating the influence of the dataset. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), New Orleans, LA, United States, 19-20 June 2022. Piscataway, NJ, United States: IEEE Computer Society. doi: 10.1109/CVPRW56347.2022.00529

  • Reduction of Feature Contamination for Hyper Spectral Image Classification

    Mahendren, Sutharsan, Fernando, Tharindu, Sridharan, Sridha, Moghadam, Peyman and Fookes, Clinton (2021). Reduction of Feature Contamination for Hyper Spectral Image Classification. 2021 Digital Image Computing: Techniques and Applications (DICTA), Gold Coast, QLD Australia, 29 November - 1 December 2021. Piscataway, NJ United States: IEEE. doi: 10.1109/dicta52665.2021.9647153

  • Locus: LiDAR-based Place Recognition using Spatiotemporal Higher-Order Pooling

    Vidanapathirana, Kavisha, Moghadam, Peyman, Harwood, Ben, Zhao, Muming, Sridharan, Sridha and Fookes, Clinton (2021). Locus: LiDAR-based Place Recognition using Spatiotemporal Higher-Order Pooling. 2021 IEEE International Conference on Robotics and Automation (ICRA), Xi'an, China, 30 May - 5 June 2021. Piscataway, NJ United States: IEEE. doi: 10.1109/icra48506.2021.9560915

  • Temporally coherent embeddings for self-supervised video representation learning

    Knights, Joshua, Harwood, Ben, Ward, Daniel, Vanderkop, Anthony, Mackenzie-Ross, Olivia and Moghadam, Peyman (2021). Temporally coherent embeddings for self-supervised video representation learning. International Conference on Pattern Recognition (ICPR), Milan, Italy, 10-15 January 2020. Washington, DC, United States: IEEE Computer Society. doi: 10.1109/icpr48806.2021.9412071

  • Point cloud segmentation using sparse temporal local attention

    Knights, Joshua, Moghadam, Peyman, Fookes, Clinton and Sridharan, Sridha (2021). Point cloud segmentation using sparse temporal local attention. Australasian Conference on Robotics and Automation (ACRA 2021), Online, 6-8 December 2021. Sydney, NSW, Australia: Australasian Robotics and Automation Association.

  • Multi-species Seagrass Detection and Classification from Underwater Images

    Raine, Scarlett, Marchant, Ross, Moghadam, Peyman, Maire, Frederic, Kettle, Brett and Kusy, Brano (2020). Multi-species Seagrass Detection and Classification from Underwater Images. 2020 Digital Image Computing: Techniques and Applications (DICTA), Melbourne, VIC Australia, 29 November - 2 December 2020. Piscataway, NJ United States: IEEE. doi: 10.1109/dicta51227.2020.9363371

  • Digital Twin for the Future of Orchard Production Systems

    Moghadam, Peyman, Lowe, Thomas and Edwards, Everard (2020). Digital Twin for the Future of Orchard Production Systems. The Third International Tropical Agriculture Conference TropAg 2019 , Brisbane, QLD Australia, 11-13 November 2019. Basel, Switzerland: MDPI. doi: 10.3390/proceedings2019036092

  • Intelligent Systems for Commercial Application in Perennial Horticulture

    Edwards, Everard and Moghadam, Peyman (2020). Intelligent Systems for Commercial Application in Perennial Horticulture. The Third International Tropical Agriculture Conference TropAg 2019 , Brisbane, QLD Australia, 11-13 November 2019. Basel, Switzerland: MDPI. doi: 10.3390/proceedings2019036059

  • Deep leaf segmentation using synthetic data

    Ward, Daniel, Moghadam, Peyman and Hudson, Nicolas (2019). Deep leaf segmentation using synthetic data. British Machine Vision Conference 2018, BMVC 2018, Newcastle, United Kingdom, 3 - 6 September 2018. BMVA Press.

  • Elastic LiDAR fusion: Dense map-centric continuous-time SLAM

    Park, Chanoh, Moghadam, Peyman, Kim, Soohwan, Elfes, Alberto, Fookes, Clinton and Sridharan, Sridha (2018). Elastic LiDAR fusion: Dense map-centric continuous-time SLAM. 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, QLD, Australia, 21-25 May 2018. Piscataway, NJ, United States: IEEE. doi: 10.1109/icra.2018.8462915

  • Non-rigid reconstruction with a single moving RGB-D camera

    Elanattil, Shafeeq, Moghadam, Peyman, Sridharan, Sridha, Fookes, Clinton and Cox, Mark (2018). Non-rigid reconstruction with a single moving RGB-D camera. 24th International Conference on Pattern Recognition (ICPR), Beijing, China, 20-24 August, 2018. Piscataway, NJ, United States: IEEE. doi: 10.1109/icpr.2018.8546201

  • Skeleton driven non-rigid motion tracking and 3D reconstruction

    Elanattil, Shafeeq, Moghadam, Peyman, Denman, Simon, Sridharan, Sridha and Fookes, Clinton (2018). Skeleton driven non-rigid motion tracking and 3D reconstruction. 2018 International Conference on Digital Image Computing: Techniques and Applications (DICTA 2018), Canberra, Australia, 10-13 December, 2018. Piscataway, NJ, United States: IEEE. doi: 10.1109/dicta.2018.8615797

  • Plant disease detection using hyperspectral imaging

    Moghadam, Peyman, Ward, Daniel, Goan, Ethan, Jayawardena, Srimal, Sikka, Pavan and Hernandez, Emili (2017). Plant disease detection using hyperspectral imaging. 2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA), Sydney, NSW Australia, 29 November - 1 December 2017. Piscataway, NJ United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/DICTA.2017.8227476

  • Plant disease detection using hyperspectral imaging

    Moghadam, Peyman, Ward, Daniel, Goan, Ethan, Jayawardena, Srimal, Sikka, Pavan and Hernandez, Emili (2017). Plant disease detection using hyperspectral imaging. International Conference on Digital Image Computing - Techniques and Applications (DICTA), Sydney, Australia, 29 November - 1 December 2017. New York, NY, United States: IEEE.

  • Probabilistic surfel fusion for dense LiDAR mapping

    Park, Chanoh, Kim, Soohwan, Moghadam, Peyman, Fookes, Clinton and Sridharan, Sridha (2017). Probabilistic surfel fusion for dense LiDAR mapping. 16th IEEE International Conference on Computer Vision (ICCV 2017), Venice, Italy, 22-29 October 2017. New York, NY, United States: Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/ICCVW.2017.285

  • Real-Time Stabilisation for Hexapod Robots

    Hoerger, Marcus, Kottege, Navinda, Bandyopadhyay, Tirthankar, Elfes, Alberto and Moghadam, Peyman (2016). Real-Time Stabilisation for Hexapod Robots. 14th International Symposium on Experimental Robotics (ISER), Morocco, 15-18 June 2014 . Heidelberg, Bermany: Springer. doi: 10.1007/978-3-319-23778-7_48

  • Terrain characterisation and gait adaptation by a hexapod robot

    Williamson, Dylan, Kottege, Navinda and Moghadam, Peyman (2016). Terrain characterisation and gait adaptation by a hexapod robot. Australasian Conference on Robotics and Automation, ACRA, Brisbane, Australia, 5-7 December 2016. Australasian Robotics and Automation Association.

  • 3D medical thermography device

    Moghadam, Peyman (2015). 3D medical thermography device. SPIE Conference on Thermosense - Thermal Infrared Applications XXXVII, Baltimore, MD, United States, 20-23 April, 2015. Bellingham, WA, United States: S P I E - International Society for Optical Engineering. doi: 10.1117/12.2177880

  • Coverage-based next best view selection

    Cunningham-Nelson, Samuel, Moghadam, Peyman, Roberts, Jonathan and Elfes, Alberto (2015). Coverage-based next best view selection. Australasian Conference on Robotics and Automation, ACRA, Canberra, Australia, 2-4 December 2015. Australasian Robotics and Automation Association.

  • Energetics-informed hexapod gait transitions across terrains

    Kottege, Navinda, Parkinson, Callum, Moghadam, Peyman, Elfes, Alberto and Singh, Surya P.N (2015). Energetics-informed hexapod gait transitions across terrains. 2015 IEEE International Conference on Robotics and Automation, ICRA 2015, Washington State Convention Center Seattle, Washington, United States, 26-30 May 2015. Piscataway NJ United States: Institute of Electrical and Electronics Engineers ( IEEE ). doi: 10.1109/ICRA.2015.7139915

  • An evaluation of multi-modal user interface elements for tablet-based robot teleoperation

    Best, Graeme and Moghadam, Peyman (2014). An evaluation of multi-modal user interface elements for tablet-based robot teleoperation. Australasian Conference on Robotics and Automation, ACRA, Melbourne, Australia, 2-4 December 2014. Australasian Robotics and Automation Association.

  • Combining motion and appearance for scene segmentation

    Borges, Paulo Vinicius Koerich and Moghadam, Peyman (2014). Combining motion and appearance for scene segmentation. 2014 IEEE International Conference on Robotics and Automation, ICRA 2014, Hong Kong, China, 31 May - 7 June. NEW YORK: Institute of Electrical and Electronics Engineers. doi: 10.1109/ICRA.2014.6906980

  • HeatWave: the next generation of thermography devices

    Moghadam, Peyman and Vidas, Stephen (2014). HeatWave: the next generation of thermography devices. Conference on Thermosense - Thermal Infrared Applications XXXVI, Baltimore, MD, United States, 5-7 May, 2014. Bellingham, WA, United States: S P I E - International Society for Optical Engineering. doi: 10.1117/12.2053950

  • Multi-sensor based gestures recognition with a smart finger ring

    Roshandel, Mehran, Munjal, Aarti, Moghadam, Peyman, Tajik, Shahin and Ketabdar, Hamed (2014). Multi-sensor based gestures recognition with a smart finger ring. 16th International Conference on Human-Computer Interaction (HCI), Heraklion, Greece, 22-27 June, 2014. Heidelberg, Germany: Springer.

  • Multi-sensor finger ring for authentication based on 3D signatures

    Roshandel, Mehran, Munjal, Aarti, Moghadam, Peyman, Tajik, Shahin and Ketabdar, Hamed (2014). Multi-sensor finger ring for authentication based on 3D signatures. 16th International Conference on Human-Computer Interaction (HCI), Heraklion, Greece, 22 - 27 June 2014. Berlin, Germany: Springer-Verlag Berlin.

  • Spectra: 3D multispectral fusion and visualization toolkit

    Moghadam, Peyman, Vidas, Stephen and Lam, Obadiah (2014). Spectra: 3D multispectral fusion and visualization toolkit. Australasian Conference on Robotics and Automation, ACRA, Melbourne, Australia, 2-4 December 2014. Australasian Robotics and Automation Association.

  • 3D thermal mapping of building interiors using an RGB-D and thermal camera

    Vidas, Stephen, Moghadam, Peyman and Bosse, Michael (2013). 3D thermal mapping of building interiors using an RGB-D and thermal camera. 2013 IEEE International Conference on Robotics and Automation, Karlsruhe, Germany, 6-10 May 2013. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/icra.2013.6630890

  • Ad hoc radiometric calibration of a thermal-infrared camera

    Vidas, Stephen and Moghadam, Peyman (2013). Ad hoc radiometric calibration of a thermal-infrared camera. International Conference on Digital Image Computing - Techniques and Applications (DICTA), Hobart, Australia, 26-28 November, 2013. Piscataway, NJ, United States: IEEE. doi: 10.1109/dicta.2013.6691478

  • Line-based extrinsic calibration of range and image sensors

    Moghadam, Peyman, Bosse, Michael and Zlot, Robert (2013). Line-based extrinsic calibration of range and image sensors. 2013 IEEE International Conference on Robotics and Automation, Karlsruhe, Germany, 6-10 May 2013. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/icra.2013.6631095

  • Terrain classification using a hexapod robot

    Best, Graeme, Moghadam, Peyman, Kottege, Navinda and Kleeman, Lindsay (2013). Terrain classification using a hexapod robot. Australasian Conference on Robotics and Automation, ACRA , Sydney, Australia, 2-4 December 2013. Australasian Robotics and Automation Association.

  • Assessing the vulnerability of magnetic gestural authentication to video-based shoulder surfing attacks

    Shirazi, Alireza Sahami, Moghadam, Peyman, Ketabdar, Hamed and Schmidt, Albrecht (2012). Assessing the vulnerability of magnetic gestural authentication to video-based shoulder surfing attacks. 30th ACM Conference on Human Factors in Computing Systems, CHI 2012, Austin, TX, United States, 5 - 10 May 2012. New York, NY, USA: ACM. doi: 10.1145/2207676.2208352

  • Magi guitar: a guitar that is played in air!

    Ketabdar, Hamed, Chang, Hengwei, Moghadam, Peyman, Roshandel, Mehran and Naderi, Babak (2012). Magi guitar: a guitar that is played in air!. 14th ACM International Conference on Human Computer Interaction with Mobile Devices and Services (MobileHCI), San Francisco, CA, United States, 21 - 24 September 2012. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/2371664.2371704

  • Magnetic signatures in air for mobile devices

    Ketabdar, Hamed, Moghadam, Peyman, Naderi, Babak and Roshandel, Mehran (2012). Magnetic signatures in air for mobile devices. 14th ACM International Conference on Human Computer Interaction with Mobile Devices and Services (MobileHCI '12), San Francisco, CA, United States, 21-24 September 2012. New York, NY, United States: ACM Press. doi: 10.1145/2371664.2371705

  • Pingu: a new miniature wearable device for ubiquitous computing environments

    Ketabdar, Hamed, Moghadam, Peyman and Roshandel, Mehran (2012). Pingu: a new miniature wearable device for ubiquitous computing environments. Sixth International Conference on Complex, Intelligent, and Software Intensive Systems , Palermo, Italy, 4-6 July 2012. doi: 10.1109/cisis.2012.123

  • Road direction detection based on vanishing-point tracking

    Moghadam, Peyman and Dong, Jun Feng (2012). Road direction detection based on vanishing-point tracking. 25th IEEE\RSJ International Conference on Intelligent Robots and Systems (IROS), Algarve, Portugal, 7-12 October, 2012. Piscataway, NJ, United States: IEEE. doi: 10.1109/iros.2012.6386089

  • Computationally efficient navigation system for unmanned ground vehicles

    Moghadam, Peyman, Salehi, Saba and Wijesoma, Wijerupage Sardha (2011). Computationally efficient navigation system for unmanned ground vehicles. 2011 IEEE Conference on Technologies for Practical Robot Applications, Woburn, MA, United States, 11-12 April 2011. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/tepra.2011.5753495

  • Collaborative Multi-Vehicle Localization and Mapping in High Clutter Environments

    Moratuwage, M. D. P., Wijesoma, W. S., Kalyan, B., Patrikalakis, Nicholas M. and Moghadam, Peyman (2010). Collaborative Multi-Vehicle Localization and Mapping in High Clutter Environments. 11th International Conference on Control, Automation, Robotics and Vision (ICARCV 2010), Singapore Singapore, Dec 07-10, 2010. NEW YORK: IEEE.

  • Towards A Fully-Autonomous Vision-based Vehicle Navigation System in Outdoor Environments

    Moghadam, Peyman, Wijesoma, Wijerupage Sardha and Moratuwage, M. D. P. (2010). Towards A Fully-Autonomous Vision-based Vehicle Navigation System in Outdoor Environments. 11th International Conference on Control, Automation, Robotics and Vision (ICARCV 2010), Singapore Singapore, Dec 07-10, 2010. NEW YORK: IEEE.

  • Online, Self-Supervised Vision-Based Terrain Classification in Unstructured Environments

    Moghadam, Peyman and Wijesoma, Wijerupage Sardha (2009). Online, Self-Supervised Vision-Based Terrain Classification in Unstructured Environments. IEEE International Conference on Systems, Man and Cybernetics, San Antonio Tx, Oct 11-14, 2009. NEW YORK: IEEE. doi: 10.1109/ICSMC.2009.5345942

  • Improving Path Planning and Mapping Based on Stereo Vision and Lidar

    Moghadam, Peyman, Wijesorna, Wijerupage Sardha and Feng, Dong Jun (2008). Improving Path Planning and Mapping Based on Stereo Vision and Lidar. 10th International Conference on Control, Automation, Robotics and Vision, Hanoi Vietnam, Dec 17-20, 2008. NEW YORK: IEEE. doi: 10.1109/ICARCV.2008.4795550

PhD and MPhil Supervision

Current Supervision

  • Generalizing Implicit Representations for Robotics Manipulation of Articulated Objects

    Doctor Philosophy — Associate Advisor

    Other advisors:

Possible Research Projects

Note for students: The possible research projects listed on this page may not be comprehensive or up to date. Always feel free to contact the staff for more information, and also with your own research ideas.

  • Self-Supervised Learning for 3D Multimodal Perception

    Potential impact of deep learning is limited due to the lack of large, annotated, and high-quality datasets in domains of interest. Annotating such datasets is laborious, costly and time-consuming. This project proposes to develop self-supervised learning systems to extract and use the relevant context given by strong prior spatio-temporal models (e.g. dense 3D reconstructions) as supervisory signals in training. This new concept will investigate model structures that encodes spatio-temporal data, and show rapid adaptation of models to new domains (few-shot learning) using trained embeddings layers (self-supervised, or prior data).

  • 3D Scene Understanding

    Simultaneous Localization and Mapping (SLAM) is a key enabling component of driverless vehicles, robotics and augmented reality. The SLAM goal is to estimate pose of the vehicle and simultaneously generate dense 3D scene reconstruction. At CSIRO we have developed and deployed state-of-the-art 3D LiDAR-based SLAM systems for the past decade. There is a new direction of research at the intersection of deep learning and geometry-based 3D SLAM. The research in this PhD programme will develop algorithms for geometry-based Deep Learning SLAM in a dynamic and unstructured environment. The PhD programme will involve the development of self or semi-supervised learning methods to address the significant weakness of most current deep networks.

  • Hyperspectral Deep Learning

    Hyperspectral cameras are currently undergoing a change from bulky and expensive equipment towards mobile and portable devices. A hyperspectral camera comprises of hundreds of bands with shortwave dependencies. Compared to conventional colour cameras (RGB bands), one could use these shortwave dependencies to design and develop a deep network for object classification, semantic segmentation and scene understanding. Both spectral and spatial relationship needs to be modelled by the deep networks simultaneously. The research in this PhD programme will develop algorithms for hyperspectral deep learning. The PhD programme will involve the development of learning with self-supervision algorithms to address the significant weakness of most current deep networks.

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ResearcherID: G-8967-2018

ORCID: 0000-0002-8169-3560

Google Scholar ID: QAVcuWUAAAAJ

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