Feras Dayoub

Dr Feras Dayoub

Senior Lecturer

School of Computer and Mathematical Sciences

Faculty of Sciences, Engineering and Technology

I work at the intersection of computer vision, machine learning, and robotics, specializing in Embodied AI and Robotic Vision as part of the Australian Institute for Machine Learning (AIML) at the University of Adelaide. I co-direct CROSSING, a French-Australian laboratory focused on human-autonomous agent teaming. In addition to my role at AIML, I hold an Adjunct position at Queensland University of Technology (QUT) and serve as an Associate Investigator with the QUT Centre for Robotics. Previously, I was a Chief Investigator at the ARC Centre of Excellence for Robotic Vision. My research is dedicated to advancing the reliable deployment of computer vision and machine learning on mobile robots in real-world environments. I worked on applied robotic vision projects spanning agricultural innovation, environmental conservation, and autonomous infrastructure monitoring. As an educator, I am passionate about teaching programming, computer vision, machine learning, and robotic perception.

  • Appointments
    Date Position Institution name
    2026 - ongoing Associate Professor Adelaide University
    2022 - ongoing Senior Lecturer University of Adelaide
    2022 - ongoing Adjunct Senior Lecturer Queensland University of Technology
    2019 - 2022 Senior Lecturer Queensland University of Technology
    2016 - 2019 Centre Research Fellow Center for Excellence in Robotic Robotics Vision (ACRV)
    2012 - 2016 Postdoctoral Research Fellow Queensland University of Technology

  • Research Interests
    Computer Vision Knowledge Representation and Machine Learning Robotics and Automation

  • Journals
    Year Citation
    2023 Pershouse, D., Dayoub, F., Miller, D., & Sünderhauf, N. (2023). Addressing the Challenges of Open-World Object Detection.
    2023 Shi, X., Qiao, Y., Wu, Q., Liu, L., & Dayoub, F. (2023). Improving Online Source-free Domain Adaptation for Object Detection by
    Unsupervised Data Acquisition.
    2023 Chapman, N. H., Dayoub, F., Browne, W., & Lehnert, C. (2023). Predicting Class Distribution Shift for Reliable Domain Adaptive Object Detection. IEEE Robotics and Automation Letters, 8(8), 1-8.
    DOI Scopus6 WoS4
    2022 Hall, D., Talbot, B., Bista, S. R., Zhang, H., Smith, R., Dayoub, F., & Sünderhauf, N. (2022). BenchBot environments for active robotics (BEAR): Simulated data for active scene understanding research. International Journal of Robotics Research, 41(3), 259-269.
    DOI Scopus3 WoS3
    2022 Rahman, Q. M., Sunderhauf, N., Corke, P., & Dayoub, F. (2022). FSNet: A Failure Detection Framework for Semantic Segmentation. IEEE Robotics and Automation Letters, 7(2), 1-8.
    DOI Scopus16 WoS14
    2022 Miller, D., Sunderhauf, N., Milford, M., & Dayoub, F. (2022). Uncertainty for identifying open-set errors in visual object detection. IEEE Robotics and Automation Letters, 7(1), 215-222.
    DOI Scopus36 WoS28
    2021 Talbot, B., Dayoub, F., Corke, P., & Wyeth, G. (2021). Robot navigation in unseen spaces using an abstract map. IEEE Transactions on Cognitive and Developmental Systems, 13(4), 791-805.
    DOI Scopus16 WoS12
    2021 Rahman, Q. M., Corke, P., & Dayoub, F. (2021). Run-time monitoring of machine learning for robotic perception: a survey of emerging trends. IEEE Access, 9, 20067-20075.
    DOI Scopus53 WoS44
    2020 Haviland, J., Dayoub, F., & Corke, P. (2020). Control of the Final-Phase of Closed-Loop Visual Grasping using
    Image-Based Visual Servoing.
    2020 Arain, B., Dayoub, F., Rigby, P., & Dunbabin, M. (2020). Close-Proximity Underwater Terrain Mapping Using Learning-based Coarse
    Range Estimation.
    2019 Skinner, J., Hall, D., Zhang, H., Dayoub, F., & Sünderhauf, N. (2019). The Probabilistic Object Detection Challenge.
    2019 Sünderhauf, N., Dayoub, F., Hall, D., Skinner, J., Zhang, H., Carneiro, G., & Corke, P. (2019). A probabilistic challenge for object detection. Nature Machine Intelligence, 1(9), 443.
    DOI WoS3
    2018 Ahn, H. S., Sa, I., & Dayoub, F. (2018). Introduction to the Special Issue on Precision Agricultural Robotics and Autonomous Farming Technologies. IEEE Robotics and Automation Letters, 3(4), 4435-4438.
    DOI Scopus5 WoS3
    2018 Hall, D., Dayoub, F., Perez, T., & McCool, C. (2018). A rapidly deployable classification system using visual data for the application of precision weed management. Computers and Electronics in Agriculture, 148, 107-120.
    DOI Scopus22 WoS16 Europe PMC2
    2017 Bawden, O., Kulk, J., Russell, R., McCool, C., English, A., Dayoub, F., . . . Perez, T. (2017). Robot for weed species plant-specific management. Journal of Field Robotics, 34(6), 1179-1199.
    DOI Scopus164 WoS138
    2017 Sa, I., Lehnert, C., English, A., McCool, C., Dayoub, F., Upcroft, B., & Perez, T. (2017). Peduncle Detection of Sweet Pepper for Autonomous Crop Harvesting-Combined Color and 3-D Information. IEEE Robotics and Automation Letters, 2(2), 765-772.
    DOI Scopus105 WoS88
    2016 Sa, I., Ge, Z., Dayoub, F., Upcroft, B., Perez, T., & McCool, C. (2016). Deepfruits: A fruit detection system using deep neural networks. Sensors (Switzerland), 16(8), 1-23.
    DOI Scopus983 WoS720 Europe PMC200
    2015 Dayoub, F., Morris, T., & Corke, P. (2015). Rubbing shoulders with mobile service robots. IEEE Access, 3, 333-342.
    DOI Scopus8 WoS8
    2011 Dayoub, F., Cielniak, G., & Duckett, T. (2011). Long-term experiments with an adaptive spherical view representation for navigation in changing environments. Robotics and Autonomous Systems, 59(5), 285-295.
    DOI Scopus50 WoS42
    - Clement, B., Dubromel, M., Santos, P. E., Sammut, K., Oppert, M., & Dayoub, F. (2024). Hybrid Navigation Acceptability and Safety. Proceedings of the AAAI Symposium Series, 2(1), 11-17.
    DOI

  • 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

  • Book Chapters
    Year Citation
    2025 Shi, X., Qiao, Y., Wu, Q., Liu, L., & Dayoub, F. (2025). Improving Online Source-Free Domain Adaptation for Object Detection by Unsupervised Data Acquisition. In A. DelBue, C. Canton, J. Pont-Tuset, & T. Tommasi (Eds.), Lecture Notes in Computer Science (Vol. 15629 LNCS, pp. 195-205). SPRINGER INTERNATIONAL PUBLISHING AG.
    DOI Scopus1 WoS1
    2017 Perez, T., Bawden, O., Kulk, J., Russell, R., McCool, C., English, A., & Dayoub, F. (2017). Overview of mechatronic design for a weed-management robotic system. In D. Zhang, & B. Wei (Eds.), Robotics and Mechatronics for Agriculture (1st ed., pp. 23-49). Boca Raton, USA: CRC Press.
    DOI
    2015 Dayoub, F., Cielniak, G., & Duckett, T. (2015). Eight weeks of episodic visual navigation inside a non-stationary environment using adaptive spherical views. In L. Mejias, P. Corke, & J. Roberts (Eds.), Springer Tracts in Advanced Robotics (Vol. 105, pp. 379-392). SPRINGER-VERLAG BERLIN.
    DOI Scopus2 WoS2
    2011 Dayoub, F., Cielniak, G., & Duckett, T. (2011). Long-term experiment using an adaptive appearance-based map for visual navigation by mobile robots. In Lecture Notes in Computer Science (Vol. 6856 LNAI, pp. 400-401). Springer Berlin Heidelberg.
    DOI Scopus1

  • Conference Papers
    Year Citation
    2025 Podgorski, S., Garg, S., Hosseinzadeh, M., Mares, L., Dayoub, F., & Reid, I. (2025). TANGO: Traversability-Aware Navigation with Local Metric Control for Topological Goals. In 2025 IEEE International Conference on Robotics and Automation (ICRA) (pp. 2399-2406). Atlanta, GA, USA: IEEE.
    DOI
    2025 Deng, J., He, T., Jiang, L., Wang, T., Dayoub, F., & Reid, I. (2025). 3D-LLaVA: Towards Generalist 3D LMMs with Omni Superpoint Transformer. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 3772-3782). IEEE.
    DOI
    2025 Lin, C. -J., Garg, S., Chin, T. -J., & Dayoub, F. (2025). Robust Scene Change Detection Using Visual Foundation Models and Cross-Attention Mechanisms. In 2025 IEEE International Conference on Robotics and Automation (ICRA) (pp. 8337-8343). Atlanta, GA, USA: IEEE.
    DOI
    2025 Zhang, W., Li, Y., Qiao, Y., Huang, S., Liu, J., Dayoub, F., . . . Liu, L. (2025). Effective Tuning Strategies for Generalist Robot Manipulation Policies. In 2025 IEEE International Conference on Robotics and Automation (ICRA) (pp. 7255-7262). Atlanta, GA, USA: IEEE.
    DOI
    2025 Abraham, S. S., Garg, S., & Dayoub, F. (2025). To Ask or Not to Ask? Detecting Absence of Information in Vision and Language Navigation. In Proceedings - 2025 IEEE Winter Conference on Applications of Computer Vision, WACV 2025 (pp. 7480-7489). Tucson, AZ, USA Funding Agency: Authors Savitha Sam Abraham Australian Institute for Machine Learning, The University of Adelaide, Australia Sourav Garg Australian Institute for Machine Learning, The University of Adelaide, Australia Feras Dayoub Australian Institute for Machine Learning, The University of Adelaide, Australia Figures References Keywords Metrics Contact IEEE to Subscribe: IEEE.
    DOI
    2025 Chapman, N. H., Lehnert, C., Browne, W., & Dayoub, F. (2025). Enhancing Embodied Object Detection with Spatial Feature Memory. In Proceedings - 2025 IEEE Winter Conference on Applications of Computer Vision, WACV 2025 (pp. 6921-6931). Tucson, AZ, USA: IEEE.
    DOI Scopus1
    2024 McLeod, S., Chng, C. K., Ono, T., Shimizu, Y., Hemmi, R., Holden, L., . . . Chin, T. J. (2024). Robust Perspective-n-Crater for Crater-based Camera Pose Estimation. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (pp. 6760-6769). Seattle: IEEE.
    DOI Scopus2
    2024 Yuan, D., Maire, F., & Dayoub, F. (2024). Temporal Attention for Cross-View Sequential Image Localization. In IEEE International Conference on Intelligent Robots and Systems (pp. 7429-7436). Abu Dhabi, United Arab Emirates: IEEE.
    DOI
    2024 Abou-Chakra, J., Rana, K., Dayoub, F., & Sünderhauf, N. (2024). Physically Embodied Gaussian Splatting: A Visually Learnt and Physically Grounded 3D Representation for Robotics. In Proceedings of Machine Learning Research Vol. 270 (pp. 513-530). Munich, Germany: ML Research Press.
    Scopus1
    2024 Wilson, S., Fischer, T., Dayoub, F., Miller, D., & Sünderhauf, N. (2024). SAFE: Sensitivity-Aware Features for Out-of-Distribution Object Detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV 2023) (pp. 23508-23519). online: IEEE.
    DOI Scopus24 WoS15
    2024 Wu, R., Wang, H., Dayoub, F., & Chen, H. T. (2024). Segment beyond View: Handling Partially Missing Modality for Audio-Visual Semantic Segmentation. In Proceedings of the AAAI Conference on Artificial Intelligence Vol. 38 (pp. 6100-6108). Online: Association for the Advancement of Artificial Intelligence (AAAI).
    DOI Scopus3 WoS2
    2024 Yuan, D., Maire, F., & Dayoub, F. (2024). Cross-Attention between Satellite and Ground Views for Enhanced Fine-Grained Robot Geo-Localization. In Proceedings - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024 (pp. 1238-1245). Online: IEEE.
    DOI Scopus4
    2024 Abou-Chakra, J., Dayoub, F., & Sunderhauf, N. (2024). ParticleNeRF: A Particle-Based Encoding for Online Neural Radiance Fields. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2024) (pp. 5963-5972). Online: IEEE.
    DOI Scopus9 WoS1
    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.
    DOI Scopus8 WoS4
    2023 Wilson, S., Fischer, T., Sunderhauf, N., & Dayoub, F. (2023). Hyperdimensional Feature Fusion for Out-of-Distribution Detection. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2023) (pp. 2643-2653). Online: IEEE.
    DOI Scopus13 WoS11
    2022 Corke, P., Dayoub, F., Hall, D., Skinner, J., & Sünderhauf, N. (2022). What Can Robotics Research Learn from Computer Vision Research?. In Proceedings of the 19th International Symposium of Robotic Research (ISRR 2019), as published in Springer Proceedings in Advanced Robotics Vol. 20 (pp. 987-1003). Cham, Switzerland: Springer.
    DOI
    2021 Moskvyak, O., Maire, F., Dayoub, F., Armstrong, A. O., & Baktashmotlagh, M. (2021). Robust Re-identification of Manta Rays from Natural Markings by Learning Pose Invariant Embeddings. In DICTA 2021 - 2021 International Conference on Digital Image Computing: Techniques and Applications (pp. 1-8). online: IEEE.
    DOI Scopus26 WoS14
    2021 Bista, S. R., Hall, D., Talbot, B., Zhang, H., Dayoub, F., & Sünderhauf, N. (2021). Evaluating the Impact of Semantic Segmentation and Pose Estimation on Dense Semantic SLAM. In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 5328-5335). online: IEEE.
    DOI Scopus9 WoS7
    2021 Miller, D., Sunderhauf, N., Milford, M., & Dayoub, F. (2021). Class anchor clustering: A loss for distance-based open set recognition. In Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV 2021) (pp. 3569-3577). online: IEEE.
    DOI Scopus128 WoS96
    2021 Moskvyak, O., Maire, F., Dayoub, F., & Baktashmotlagh, M. (2021). Keypoint-aligned embeddings for image retrieval and re-identification. In Proceedings - 2021 IEEE Winter Conference on Applications of Computer Vision, WACV 2021 (pp. 676-685). online: IEEE.
    DOI Scopus24 WoS19
    2021 Zhang, H., Wang, Y., Dayoub, F., & Sünderhauf, N. (2021). VarifocalNet: An IoU-aware Dense Object Detector. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 8510-8519). online: IEEE.
    DOI Scopus903 WoS746
    2021 Rahman, Q. M., Sunderhauf, N., & Dayoub, F. (2021). Per-frame mAP Prediction for Continuous Performance Monitoring of Object Detection during Deployment. In Proceedings of the IEEE Winter Conference on Applications of Computer Vision Workshops (WACVW 2021) (pp. 152-160). online: IEEE.
    DOI Scopus16 WoS15
    2021 Rahman, Q. M., Sünderhauf, N., & Dayoub, F. (2021). Online Monitoring of Object Detection Performance During Deployment. In Proceedings of the 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021) (pp. 4839-4845). online: IEEE.
    DOI Scopus10 WoS9
    2021 Moskvyak, O., Maire, F., Dayoub, F., & Baktashmotlagh, M. (2021). SEMI-SUPERVISED KEYPOINT LOCALIZATION. In ICLR 2021 - 9th International Conference on Learning Representations (pp. 1-11). Virtual only: Open Review.
    Scopus15
    2020 Moskvyak, O., Maire, F., Dayoub, F., & Baktashmotlagh, M. (2020). Learning Landmark Guided Embeddings for Animal Re-identification. In 2020 IEEE Winter Applications of Computer Vision Workshops (WACVW) (pp. 12-19). online: IEEE.
    DOI Scopus10 WoS11
    2020 Hall, D., Dayoub, F., Skinner, J., Zhang, H., Miller, D., Corke, P., . . . Sunderhauf, N. (2020). Probabilistic object detection: Definition and evaluation. In Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020 (pp. 1020-1029). online: IEEE.
    DOI Scopus91 WoS67
    2019 Halodova, L., Dvorrakova, E., Majer, F., Vintr, T., Mozos, O. M., Dayoub, F., & Krajnik, T. (2019). Predictive and adaptive maps for long-term visual navigation in changing environments. In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 7033-7039). Macau, China: IEEE.
    DOI Scopus22 WoS18
    2019 Miller, D., Dayoub, F., Milford, M., & Sunderhauf, N. (2019). Evaluating merging strategies for sampling-based uncertainty techniques in object detection. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2019) (pp. 2348-2354). online: IEEE.
    DOI Scopus96 WoS73
    2019 Rahman, Q. M., Sunderhauf, N., & Dayoub, F. (2019). Did You Miss the Sign? A False Negative Alarm System for Traffic Sign Detectors. In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 3748-3753). online: IEEE.
    DOI Scopus23 WoS23
    2019 Miller, D., Sünderhauf, N., Zhang, H., Hall, D., & Dayoub, F. (2019). Benchmarking sampling-based probabilistic object detectors. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Vol. 2019-June (pp. 42-45).
    Scopus12
    2018 Abbas, A., Maire, F., Shirazi, S., Dayoub, F., & Eich, M. (2018). A dynamic planner for object assembly tasks based on learning the spatial relationships of its parts from a single demonstration. In T. Mitrovic, B. Xue, & X. Li (Eds.), Proceedings of AI 2018: Advanced in Artificial Intelligence 31st Australasian Joint Conference Vol. 11320 LNAI (pp. 759-765). Wellington, New Zealand: Springer International Publishing.
    DOI Scopus2
    2018 McFadyen, A., Dayoub, F., Martin, S., Ford, J., & Corke, P. (2018). Assisted Control for Semi-Autonomous Power Infrastructure Inspection Using Aerial Vehicles. In IEEE International Conference on Intelligent Robots and Systems (pp. 5719-5726). online: IEEE.
    DOI Scopus3 WoS2
    2018 Miller, D., Nicholson, L., Dayoub, F., & Sunderhauf, N. (2018). Dropout Sampling for Robust Object Detection in Open-Set Conditions. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2018) (pp. 3243-3249). Brisbane, Australia: IEEE.
    DOI Scopus215 WoS173
    2018 Abbas, A., Maire, F., Dayoub, F., & Shirazi, S. (2018). Combining learning from demonstration and search algorithm for dynamic goal-directed assembly task planning. In ACRA 2018 Proceedings Vol. 2018-December. Online: Australian Robotics and Automation Association.
    2017 Dayoub, F., Sunderhauf, N., & Corke, P. I. (2017). Episode-Based Active Learning with Bayesian Neural Networks. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW 2017) Vol. 2017-July (pp. 498-500). Honolulu, Hawaii, USA: IEEE.
    DOI Scopus7 WoS5
    2017 Hall, D., Dayoub, F., Kulk, J., & McCool, C. (2017). Towards unsupervised weed scouting for agricultural robotics. In Proceedings - IEEE International Conference on Robotics and Automation (pp. 5223-5230). online: IEEE.
    DOI Scopus36
    2017 Hall, D., Dayoub, F., Perez, T., & McCool, C. (2017). A transplantable system for weed classification by agricultural robotics. In 2017 IEEE International Conference on Intelligent Robots and Systems Vol. 2017-September (pp. 5174-5179). Vancouver, BC, Canada: IEEE.
    DOI Scopus5 WoS4
    2016 Sunderhauf, N., Dayoub, F., McMahon, S., Talbot, B., Schulz, R., Corke, P., . . . Milford, M. (2016). Place categorization and semantic mapping on a mobile robot. In Proceedings - IEEE International Conference on Robotics and Automation Vol. 2016-June (pp. 5729-5736). online: IEEE.
    DOI Scopus126 WoS94
    2016 McCool, C., Sa, I., Dayoub, F., Lehnert, C., Perez, T., & Upcroft, B. (2016). Visual detection of occluded crop: For automated harvesting. In Proceedings - IEEE International Conference on Robotics and Automation Vol. 2016-June (pp. 2506-2512). Stockholm, Sweden: IEEE.
    DOI Scopus68 WoS54
    2016 Talbot, B., Lam, O., Schulz, R., Dayoub, F., Upcroft, B., & Wyeth, G. (2016). Find my office: Navigating real space from semantic descriptions. In Proceedings - IEEE International Conference on Robotics and Automation Vol. 2016-June (pp. 5782-5787). online: IEEE.
    DOI Scopus19 WoS12
    2015 Schulz, R., Talbot, B., Lam, O., Dayoub, F., Corke, P., Upcroft, B., & Wyeth, G. (2015). Robot navigation using human cues: A robot navigation system for symbolic goal-directed exploration. In Proceedings IEEE International Conference on Robotics and Automation Vol. 2015-June (pp. 1100-1105). Seattle, WA: IEEE COMPUTER SOC.
    DOI Scopus28 WoS16
    2015 Dayoub, F., Dunbabin, M., & Corke, P. (2015). Robotic detection and tracking of Crown-of-Thorns starfish. In IEEE International Conference on Intelligent Robots and Systems Vol. 2015-December (pp. 1921-1928). Hamburg, GERMANY: IEEE.
    DOI Scopus33 WoS23
    2015 Sünderhauf, N., Shirazi, S., Jacobson, A., Dayoub, F., Pepperell, E., Upcroft, B., & Milford, M. (2015). Place recognition with convnet landmarks: Viewpoint-robust, condition-robust, training-free. In Proceedings of the Robotics: Science and Systems XI Conference (RSS 2015) Vol. 11 (pp. 1-10). online: Robotics: Science and Systems Foundation.
    DOI Scopus329 WoS198
    2015 Hall, D., McCool, C., Dayoub, F., Sünderhauf, N., & Upcroft, B. (2015). Evaluation of features for leaf classification in challenging conditions. In Proceedings 2015 IEEE Winter Conference on Applications of Computer Vision Wacv 2015 (pp. 797-804). HI, Waikoloa: IEEE.
    DOI Scopus114 WoS79
    2015 Sünderhauf, N., Shirazi, S., Dayoub, F., Upcroft, B., & Milford, M. (2015). On the performance of ConvNet features for place recognition. In IEEE International Conference on Intelligent Robots and Systems Vol. 2015-December (pp. 4297-4304). Hamburg, GERMANY: IEEE.
    DOI Scopus473 WoS386
    2015 Lam, O., Dayoub, F., Schulz, R., & Corke, P. (2015). Automated topometric graph generation from floor plan analysis. In Australasian Conference on Robotics and Automation Acra.
    Scopus9
    2014 Lam, O., Dayoub, F., Schulz, R., & Corke, P. (2014). Text recognition approaches for indoor robotics: A comparison. In Australasian Conference on Robotics and Automation Acra Vol. 02-04-December-2014.
    Scopus4
    2014 Morris, T., Dayoub, F., Corke, P., & Upcroft, B. (2014). Simultaneous localization and planning on multiple map hypotheses. In IEEE International Conference on Intelligent Robots and Systems (pp. 4531-4536). Chicago, IL: IEEE.
    DOI Scopus5 WoS5
    2014 Morris, T., Dayoub, F., Corke, P., Wyeth, G., & Upcroft, B. (2014). Multiple map hypotheses for planning and navigating in non-stationary environments. In Proceedings IEEE International Conference on Robotics and Automation (pp. 2765-2770). Hong Kong, PEOPLES R CHINA: IEEE.
    DOI Scopus19 WoS15
    2013 Dayoub, F., Morris, T., Upcroft, B., & Corke, P. (2013). Vision-only autonomous navigation using topometric maps. In N. Amato (Ed.), IEEE International Conference on Intelligent Robots and Systems (pp. 1923-1929). JAPAN, Tokyo: IEEE.
    DOI Scopus35 WoS23
    2013 Dayoub, F., Morris, T., Upcroft, B., & Corke, P. (2013). One Robot, eight hours, and twenty four thousand people. In Australasian Conference on Robotics and Automation Acra.
    Scopus1
    2010 Dayoub, F., Duckett, T., & Cielniak, G. (2010). Short- and long-term adaptation of visual place memories for mobile robots. In Proceedings of the International Symposium on Remembering Who We are Human Memory for Artificial Agents A Symposium at the Aisb 2010 Convention (pp. 21-26).
    Scopus2
    2008 Dayoub, F., & Duckett, T. (2008). An adaptive appearance-based map for long-term topological localization of mobile robots. In R. Chatila, A. Kelly, & J. P. Merlet (Eds.), 2008 IEEE Rsj International Conference on Intelligent Robots and Systems Iros (pp. 3364-3369). Nice, FRANCE: IEEE.
    DOI Scopus79 WoS55

  • Preprint
    Year Citation
    2024 Mallick, P., Dayoub, F., & Sherrah, J. (2024). Wasserstein Distance-based Expansion of Low-Density Latent Regions for
    Unknown Class Detection.
    2024 Yuan, D., Maire, F., & Dayoub, F. (2024). Temporal Attention for Cross-View Sequential Image Localization.
    2024 Lin, C. -J., Garg, S., Chin, T. -J., & Dayoub, F. (2024). Robust Scene Change Detection Using Visual Foundation Models and
    Cross-Attention Mechanisms.
    2024 Bockman, J., Howe, M., Orenstein, A., & Dayoub, F. (2024). AARK: An Open Toolkit for Autonomous Racing Research.
    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 Abraham, S. S., Garg, S., & Dayoub, F. (2024). To Ask or Not to Ask? Detecting Absence of Information in Vision and
    Language Navigation.
    2024 Abou-Chakra, J., Rana, K., Dayoub, F., & Sünderhauf, N. (2024). Physically Embodied Gaussian Splatting: A Realtime Correctable World
    Model for Robotics.
    2023 Holden, L., Dayoub, F., Harvey, D., & Chin, T. -J. (2023). Federated Neural Radiance Fields.
    2023 Wu, R., Wang, H., Dayoub, F., & Chen, H. -T. (2023). Segment Beyond View: Handling Partially Missing Modality for
    Audio-Visual Semantic Segmentation.
    2022 Wilson, S., Fischer, T., Dayoub, F., Miller, D., & Sünderhauf, N. (2022). SAFE: Sensitivity-Aware Features for Out-of-Distribution Object
    Detection.

  • Current Higher Degree by Research Supervision (University of Adelaide)
    Date Role Research Topic Program Degree Type Student Load Student Name
    2025 Co-Supervisor Exploring Human-Robot Interaction as a Collaborative Model for One-to-Many Teaming in Aerospace Environments Doctor of Philosophy Doctorate Full Time Mrs Donna Lee Duffy
    2025 Co-Supervisor Low-Cost Implementation of Visual Pseudo-Tactile and Multitasking Imitation Learning Master of Philosophy Master Full Time Mr Yukun Chen
    2024 Principal Supervisor Foundation models for goal-oriented reinforcement learning and intrinsic exploration Master of Philosophy Master Full Time Mr Dustin Wyly Craggs
    2024 Principal Supervisor Robust Machine Learning Techniques for RF Signal Classification on Sparse and Noisy Digitally Sampled Radar Data Master of Philosophy Master Full Time Mr Sebastian Luke McCormack Cocks
    2024 Co-Supervisor Hand-held Object Identification, Segmentation, and Tracking in the Wild Doctor of Philosophy Doctorate Full Time Mr Huy Anh Nguyen
    2024 Principal Supervisor Data-driven physically plausible dexterous manipulation Doctor of Philosophy Doctorate Full Time Mr King Hang Wong
    2023 Principal Supervisor 3D Scene Understanding and Change Tracking Doctor of Philosophy Doctorate Full Time Mr Chun-Jung Lin
    2023 Co-Supervisor 3D indoor Scene Reconstruction Doctor of Philosophy Doctorate Full Time Mr Wenbo Zhang
    2022 Co-Supervisor Enhancing Mars rovers using AI-enabled robotic vision Doctor of Philosophy Doctorate Full Time Mr Lachlan William Holden

  • Past Higher Degree by Research Supervision (University of Adelaide)
    Date Role Research Topic Program Degree Type Student Load Student Name
    2023 - 2025 Principal Supervisor Domain Adaptation Object Detection for Mobile Robots Master of Philosophy Master Full Time Mr Xiangyu Shi
    2022 - 2024 Co-Supervisor Towards Pedestrian Safety Augmented Reality System Master of Philosophy Master Full Time Mr Renjie Wu
  • Position: Senior Lecturer
  • Email: feras.dayoub@adelaide.edu.au
  • Campus: North Terrace
  • Org Unit: Australian Institute for Machine Learning - Projects
Connect With Me
External Profiles

Other Links