Papers
9
Total Citations
111
H-Index
5
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
Top Papers
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- 5GPU-Mapping: Robotic Map Building with Graphical Multiprocessors8 citations · 2013
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- 7EFFICIENT GLOBAL LOCALIZATION BY SEARCHING A BIT-ENCODED GRAPH3 citations · 2007
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- 9Fast Processing of Grid Maps using Graphical Multiprocessors2 citations · 2010