Papers
15
Total Citations
464
H-Index
11
About
Chi Hay Tong is a leading researcher in robotics and autonomous systems, with a focus on state estimation, perception, and energy-efficient navigation. Their major contributions include pioneering the use of Gaussian process (GP) regression for batch continuous-time trajectory estimation, a framework that treats trajectories as exactly sparse GPs to achieve high accuracy in continuous-discrete estimation problems—a work that has garnered over 200 citations across key papers. Tong also developed the Canadian Planetary Emulation Terrain 3D Mapping Dataset, a widely used resource of 272 laser scans for planetary rover algorithm development (54 citations). Their research on probabilistic prediction of perception performance, such as in "Learn from Experience" (37 citations), addresses reliability in autonomous decision-making under challenging conditions. Notable achievements include advancing visual navigation with lidar-intensity-image pipelines for low-light environments and introducing scheduled perception strategies to reduce robot energy consumption during path following. Tong’s work on embedding localiser performance models in maps further enhances autonomous system robustness. With a total of over 400 citations, their contributions are instrumental in making autonomous systems more reliable, efficient, and adaptable to real-world environments.
Research Focus
Key Achievements
Top Papers
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- 3The Canadian planetary emulation terrain 3D mapping dataset54 citations · 2013
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- 5Into Darkness: Visual Navigation Based on a Lidar-Intensity-Image Pipeline34 citations · 2016
- 6Scheduled perception for energy-efficient path following26 citations · 2015
- 7Gaussian Process Gauss-Newton for 3D laser-based Visual Odometry25 citations · 2013
- 8Robotics: Science and Systems X23 citations · 2014
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- 10Fit for Purpose? Predicting Perception Performance Based on Past Experience14 citations · 2017