Toby P. Breckon
Papers
6
Total Citations
127
H-Index
5
About
Toby P. Breckon is a leading researcher in computer vision and robotics, with a focus on scene understanding, autonomous navigation, and robotic manipulation. His work bridges geometric and semantic perception, enabling robots to operate robustly in real-world environments. Breckon's key contributions include cross-spectral visual SLAM, which allows seamless sensor handover for robots navigating under varying lighting conditions (42 citations), and multi-task learning frameworks that jointly predict depth and semantic labels for applications like autonomous driving (42 citations). He has also advanced sparse depth completion for 3D scene understanding, video stabilization for tele-operated robots in hazardous environments, and low-cost omnidirectional platforms for 3D mapping. More recently, Breckon has explored generative grasping models, improving robotic manipulation of unseen objects. With over 125 citations across his most-cited works, his research has significant impact on both academic theory and practical deployment in inspection, navigation, and autonomous systems. Breckon's work is characterized by its focus on robustness, real-time performance, and integration of multiple sensing modalities, making him a key figure in the evolution of intelligent robotic systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5
- 6Evaluating Gaussian Grasp Maps for Generative Grasping Models3 citations · 2022