Lecturer (Assistant Professor) and Chief Investigator
Queensland University of Technology
I am passionate about my overarching research goal: providing robots with perceptual abilities that allow safe, intelligent interactions with humans in real-world environments. To develop these perceptual abilities, I believe it is useful to study the principles of the human, animal and insect visual systems. I use these principles to develop new computer vision and machine learning algorithms and validate their effectiveness in intelligent robotic systems. I am enthusiastic about this forward/reverse engineering approach, which combines concepts from computer science and engineering with those from biology and social sciences, as it offers the dual benefit of uncovering principles inherent in the animal visual system, as well as applying these principles to its artificial counterpart. I am a recipient of two best thesis awards and two best poster awards and have acquired grants in excess of 400,000 AUD.
Our results show that our approach achieves #competitive performance when compared to several baseline methods (EventVLAD, Event-VPR, Ensemble-Event-VPR), and is particularly well suited for compute- and energy-constrained platforms.
We explore the distinctiveness of event streams from a small subset of pixels (in the tens or hundreds) for the #VPR task. Using sparse (over image coordinates) but varying (variance over the #events per pixel) pixels enables frequent and computationally cheap location estimates.
One of my favourite topics, active sensing, discussed by @mpopovic514 in fully autonomous #robots and #drones. Slides so far still use GPs, I wonder if the next slides will reveal implicit representations. @2022Iros