Over the past half century, robots have changed the way we live and work—particularly in structured environments such as factories. However, even the most modern and sophisticated robots still cannot deal with unstructured or unpredictable environments such as farms, mines and roads.
Our research is part of the continuing robotics revolution to create next-generation robots that can function in real complex and dynamic environment. We focus on the robotic sciences; the underpinning scientific theories behind the functionality of robots. We conduct fundamental research applicable to unmanned vehicle technologies, particularly aerial robots. For example, we explore the systems that enable autonomous navigation and 3D mapping, multi-vehicle formation control, high performance human teleoperation of remote vehicles, and aerial disaster-zone reconnaissance. One of our strengths is that we are co-located with the Computer Vision, Networked Systems and Quantum Cybernetics research areas, creating a dynamic environment that supports breakthrough interdisciplinary research.
We have cutting-edge flying facilities and rapid prototyping equipment that enable in-house manufacturing and testing of robotic systems. With exposure to the latest technologies, students can gain job-ready skills. The establishment of an ARC Centre for Robotics Vision (ACRV) node at ANU provides student scholarships and research fellowships as well as a link with the cutting edge research in robotic vision world-wide.
Explore our available student research projects below and if you’d like to discuss opportunities for collaboration or funding, please email us.
T. Stoffregen*, C. Scheerlinck*, D. Scaramuzza, T. Drummond, N. Barnes, L. Kleeman, R. Mahony, “How To Train Your Event Camera Neural Network”, arXiv, 2020.
C. Scheerlinck, H. Rebecq, D. Gehrig, N. Barnes, R. Mahony, D. Scaramuzza, “Fast Image Reconstruction with an Event Camera”, Winter Conference on Applications of Computer Vision (WACV), 2020, pp. 156-163.
C. Scheerlinck*, H. Rebecq*, T. Stoffregen, N. Barnes, R. Mahony, D. Scaramuzza, “CED: Color Event Camera Dataset”, Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2019.
L. Pan, C. Scheerlinck, X. Yu, R. Hartley, M. Liu, Y. Dao “Bringing a Blurry Frame Alive at High Frame-Rate with an Event Camera”, Conference on Computer Vision and Pattern Recognition, 2019.
C. Scheerlinck, N. Barnes, R. Mahony, “Asynchronous Spatial Image Convolutions for Event Cameras”, IEEE Robotics and Automation Letters, 4(2), April 2019, pp. 816-822.
C. Scheerlinck, N. Barnes, R. Mahony, “Continuous-time Intensity Estimation Using Event Cameras”, Asian Conference on Computer Vision (ACCV), Perth, 2018, pp. 308-324.
JL. Stevens, R. Mahony. "Vision based forward sensitive reactive control for a quadrotor VTOL.", IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018, pp. 5232-5238.
M. Henein, G. Kennedy, R. Mahony, V. Ila, "Exploiting Rigid Body Motion for SLAM in Dynamic Environments", IEEE International Conference on Robotics and Automation Workshops (ICRAW), 2018.
M. Henein, M. Abello, V. Ila, R. Mahony, "Exploring the effect of meta-structural information on the global consistency of SLAM.", IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017, pp. 1616-1623.