Papers
25
Total Citations
810
H-Index
12
About
Jorge Lobo is a robotics and computer vision researcher whose work sits at the intersection of multimodal sensing, autonomous perception, and human-robot interaction. He is perhaps best known for his pioneering contributions to the fusion of inertial and visual sensing systems, a research direction that has proven foundational for autonomous robots and mobile platforms. His 2007 tutorial, "An Introduction to Inertial and Visual Sensing," has accumulated 285 citations and remains an essential reference for researchers entering the field, while his earlier work exploring gravity as a vertical reference for vision-inertial cooperation (137 citations) demonstrated practical, elegant solutions to robust 3D scene understanding. Lobo's research has consistently drawn inspiration from biological perception, particularly the dorsal pathway of the human brain, informing his development of Bayesian frameworks for active artificial perception. He has also made notable contributions to robotic grasping, investigating how robots can learn from observing human object manipulation — work that bridges cognitive science and engineering. His involvement in the CHOPIN project further reflects a commitment to socially impactful robotics, deploying autonomous systems to assist first responders in disaster scenarios. Across more than a decade of prolific output, Lobo's research has shaped how robots sense, interpret, and interact with the physical world.
Research Focus
Key Achievements
Top Papers
- 1An Introduction to Inertial and Visual Sensing285 citations · 2007
- 2Vision and inertial sensor cooperation using gravity as a vertical reference137 citations · 2003
- 3
- 4A Bayesian framework for active artificial perception48 citations · 2012
- 5Inertial navigation system for mobile land vehicles45 citations · 2002
- 6Bayesian real-time perception algorithms on GPU37 citations · 2010
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- 9World feature detection and mapping using stereovision and inertial sensors20 citations · 2003
- 10Integration of inertial information with vision17 citations · 2002