Papers
160
Total Citations
1,546
H-Index
18
About
Paulo Costa is a robotics researcher whose work spans mobile robot localization, omnidirectional robot modeling, simulation environments, and human-robot collaboration. His most influential contribution, a comparative study of map-matching algorithms for robot self-localization (2018, 87 citations), has become a key reference for researchers navigating the complex landscape of techniques such as Perfect Match, ICP, and NDT. His early foundational work on dynamical modeling and parameter estimation for omnidirectional mobile robots (2009, 67 citations; 2008, 27 citations) provided the field with practical, experimentally grounded frameworks that continue to inform robot design. Costa also developed SimTwo, a realistic robot simulator (2011, 42 citations) designed to bridge the gap between virtual testing and real-world deployment — a tool that has proven particularly valuable in educational robotics contexts, as highlighted in his systematic literature reviews (2021, 39 citations; 2022, 31 citations). His research on stereo-based tool tracking for programming by demonstration (2014, 48 citations) and robust human position estimation in collaborative robotic cells (2020, 41 citations) further demonstrates his commitment to safe and intuitive human-robot interaction. Across more than a decade of prolific output, Costa has meaningfully shaped both the theoretical foundations and practical applications of autonomous mobile robotics.
Research Focus
Key Achievements
Top Papers
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- 5Robust human position estimation in cooperative robotic cells41 citations · 2020
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- 8A Localization Method Based on Map-Matching and Particle Swarm Optimization35 citations · 2013
- 9Systematic Mapping Literature Review of Mobile Robotics Competitions31 citations · 2022
- 10DYNAMICAL MODELS FOR OMNI-DIRECTIONAL ROBOTS WITH 3 AND 4 WHEELS27 citations · 2008