Papers
11
Total Citations
3,861
H-Index
10
About
Henry Carrillo is a prominent robotics researcher whose work sits at the intersection of autonomous navigation, probabilistic mapping, and active perception. He is best known for his foundational contributions to Simultaneous Localization and Mapping (SLAM), the field concerned with enabling robots to construct environmental maps while simultaneously tracking their own position within them. Carrillo's most celebrated contribution is his co-authorship of the landmark 2016 survey "Past, Present, and Future of Simultaneous Localization and Mapping," which has amassed over 3,150 citations and remains one of the most comprehensive and widely referenced works in autonomous robotics. His 2023 survey on Active SLAM — which addresses how robots can intelligently plan their own motion to maximize mapping accuracy — has already earned nearly 300 citations, underscoring his continued leadership in this evolving area. A recurring theme across his research is the application of information-theoretic principles, particularly Shannon and Rényi entropy, to guide robotic exploration and uncertainty quantification. His rigorous comparisons of optimality criteria for active SLAM systems have shaped how researchers evaluate and design these algorithms. Carrillo has also made contributions to robot calibration and semantic place recognition, reflecting the breadth of his expertise across intelligent autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age3,158 citations · 2016
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- 3On the comparison of uncertainty criteria for active SLAM143 citations · 2012
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- 7On the monotonicity of optimality criteria during exploration in active SLAM33 citations · 2015
- 8On task-oriented criteria for configurations selection in robot calibration19 citations · 2013
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- 10Place categorization using sparse and redundant representations10 citations · 2014