Dr Peyman Moghadam

Adjunct Associate Professor

School of Information Technology and Electrical Engineering
Faculty of Engineering, Architecture and Information Technology

Overview

Dr Peyman Moghadam is leading Embodied AI research team at the CSIRO Robotics and Autonomous Systems group. He is a Principal Research Scientist at CSIRO, Data61. Currently he is leading a Spatiotemporal AI research portfolio at CSIRO's Machine Learning and Artificial Intelligence, Future Science Platform He is an Adjunct Professor at the Queensland University of Technology and Adjunct Senior Fellow at the University of Queensland. He received his PhD in Robotics from the Nanyang Technological University (Singapore) in 2011. Before joining CSIRO, he has worked in number of top leading organizations such as the Deutsche Telekom Laboratories (Germany), the Singapore-MIT Alliance for Research and Technology (Singapore). His current research interests focus on Self-Supervised Learning and Embodied Intelligence for Robotics. Professor Moghadam has led several large-scale multidisciplinary projects and he has won numerous awards for his innovations including CSIRO Julius Career award, National and Queensland iAward for Research and Development, the Lord Mayor’s Budding Entrepreneurs Award.

Research Interests

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

Publications

  • Lowe, Thomas, Moghadam, Peyman, Edwards, Everard and Williams, Jason (2021). Canopy density estimation in perennial horticulture crops using 3D spinning lidar SLAM. Journal of Field Robotics, 38 (4) rob.22006, 598-618. doi: 10.1002/rob.22006

  • Park, Chanoh, Moghadam, Peyman, Williams, Jason, Kim, Soohwan, Sridharan, Sridha and Fookes, Clinton (2021). Elasticity Meets Continuous-Time: Map-Centric Dense 3D LiDAR SLAM. IEEE Transactions on Robotics, 38 (2), 1-20. doi: 10.1109/tro.2021.3096650

  • 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

View all Publications

Available Projects

  • 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).

  • 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 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

Journal Article

Conference Publication

  • 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

  • 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

  • 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

  • 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.

  • 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

  • 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

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

  • 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.

  • 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

  • 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

  • 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.

  • 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

  • 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.

  • 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

  • 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.

  • 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

  • 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

  • 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.

  • 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.

  • 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.

  • 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

  • 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

  • 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

  • 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.

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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.

  • 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.

  • 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

  • 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

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.

  • 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).

  • 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 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.