Dexter Ong
Papers
2
Total Citations
25
H-Index
1
About
Dexter Ong is a robotics researcher whose work lies at the intersection of autonomous aerial systems, dense 3D mapping, and real-time navigation. His primary contributions focus on enabling robots to perceive and interact with complex, unstructured environments—particularly in forestry and exploration tasks. In his highly cited 2024 paper, “UAVs for forestry: Metric-semantic mapping and diameter estimation with autonomous aerial robots” (24 citations), Ong developed a framework that allows drones to autonomously map forest environments while estimating tree diameters, combining metric and semantic understanding for ecological monitoring. Building on this, his 2025 work, “RT-GuIDE: Real-Time Gaussian Splatting for Information-Driven Exploration” (1 citation), introduces a novel active mapping pipeline that leverages Gaussian splatting to construct dense, real-time maps. He further advances the field by proposing a GPU-accelerated motion planning algorithm that exploits these maps for efficient, onboard navigation. Ong’s research is notable for pushing the boundaries of real-time, onboard autonomy, demonstrating how cutting-edge neural rendering techniques can be deployed on resource-constrained aerial platforms. His work is already shaping the future of autonomous exploration in challenging, information-rich environments.
Research Focus
Key Achievements
Top Papers
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- 2