About

Juan Irving Vasquez-Gomez is a robotics researcher whose work has fundamentally advanced the fields of autonomous 3D object reconstruction and robotic path planning. His career has centered on solving one of robotics' most enduring challenges: how a robot can intelligently decide *where to look next* in order to build complete, accurate models of unknown objects. Vasquez-Gomez is best known for pioneering next-best-view (NBV) planning algorithms, a body of work spanning over a decade that has collectively garnered hundreds of citations. His landmark 2014 paper on volumetric NBV planning under positioning error (128 citations) introduced rigorous probabilistic approaches to handle real-world sensor uncertainty, while his 2020 contribution on supervised learning for NBV (83 citations) brought modern machine learning techniques to bear on the problem. His 2014 work extending NBV planning to mobile manipulator robots demonstrated practical deployment in complex robotic systems, bridging theory and application. Beyond reconstruction, Vasquez-Gomez has made meaningful contributions to coverage path planning for service robotics, including efficient strategies for 2D convex regions (68 citations) and divide-and-conquer sweeping algorithms. With over 500 total citations, his research continues to shape how autonomous robots perceive and interact with their physical environments.

Research Focus

Key Achievements

10
H-Index
13
Papers
524
Total Citations
40
Avg Citations/Paper
🏆 Most Cited Paper
Volumetric Next-best-view Planning for 3D Object Reconstruction with Positioning Error
128 citations · 2014
📈 Most Prolific Year: 2014 (2 Papers)
🤝 Key Collaborators: 19
🏛 Institutions: National Institute of Astrophysics, Optics and Electronics, Consejo Nacional de Humanidades, Ciencias y Tecnologías, Instituto Politécnico Nacional, Consejo Nacional de Ciencia y Tecnología

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 16 days ago