About

Pablo San Segundo is a leading researcher in combinatorial optimization and robotics, whose work bridges the gap between theoretical algorithms and practical autonomous systems. His primary research areas include maximum clique problems (MCP), simultaneous localization and mapping (SLAM), and GPU-accelerated robotics. San Segundo’s most significant contribution is the development of a new branch-and-bound algorithm for the maximum edge-weighted clique problem (2019, 29 citations), which advanced the state of the art in solving complex graph-based optimization tasks. He also pioneered robust data association methods for mobile robotics, introducing a sparse bit-optimized maximum clique algorithm (2013, 26 citations) that efficiently handles correspondence graphs in real-time localization. His earlier work on bit-parallel MCP solvers (2008, 25 citations) provided fast exact feature-based data correspondence search, while his Dual FastSLAM framework (2008, 13 citations) offered a novel factorization of particle filters for SLAM. Notably, San Segundo explored the use of graphical processing units (GPUs) for robotic map building (2013, 8 citations), demonstrating how massively parallel computing can accelerate 2-D mapping. His research has earned over 100 citations, reflecting its impact on both algorithmic theory and practical robotics applications.

Research Focus

Key Achievements

5
H-Index
9
Papers
111
Total Citations
12
Avg Citations/Paper
🏆 Most Cited Paper
A new branch-and-bound algorithm for the maximum edge-weighted clique problem
29 citations · 2019
📈 Most Prolific Year: 2013 (2 Papers)
🤝 Key Collaborators: 11
🏛 Institutions: Centre for Automation and Robotics, Consejo Superior de Investigaciones Científicas, Universidad Politécnica de Madrid

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago