Jose-Luis Matez-Bandera
Instituto de Investigación Biomédica de Málaga, Universidad de Málaga
Papers
5
Total Citations
38
H-Index
3
About
Jose-Luis Matez-Bandera is a leading researcher in robotic perception and semantic mapping, with a focus on enabling mobile robots to understand and navigate complex indoor environments. His work bridges computer vision, active perception, and large language models (LLMs) to create more intelligent and autonomous systems. A key contribution is the development of attention mechanisms for efficient place categorization, allowing robots to actively select informative viewpoints—a method that has garnered 16 citations and advanced real-world robotic scene understanding. Matez-Bandera also pioneered Sigma-FP, a robust 3D floor plan reconstruction technique that handles uncertainty from RGB-D data, and LTC-Mapping, which ensures long-term consistency in object-oriented semantic maps by preventing duplicate object instances. Most notably, his recent research on agentic workflows for LLM-based robotic planning (2025, 13 citations) addresses the critical challenge of hallucination in AI, proposing frameworks that improve reasoning accuracy for object-centered tasks. With over 38 citations across his top papers, Matez-Bandera’s work is shaping the future of autonomous robotics, making him a key figure in the integration of semantic reasoning and active perception.
Research Focus
Key Achievements
Top Papers
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