Dr. Jose Luis Matez Bandera

@article{matezbandera2026,
title = {Voxeland: Probabilistic Instance-Aware Semantic Mapping with Evidence-based Uncertainty Quantification},
author = {Matez-Bandera, J. L and Ojeda, Pepe and Monroy, Javier and Gonzalez-Jimenez, Javier and Ruiz-Sarmiento, Jose-Raul},
url = {https://arxiv.org/abs/2411.08727},
doi = {10.1016/j.robot.2026.105668},
issn = {0921-8890},
year  = {2026},
date = {2026-01-01},
urldate = {2026-01-01},
journal = {Robotics and Autonomous Systems},
volume = {205},
pages = {105668},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{moncada2025robotics,
title = {Agentic Workflows for Improving Large Language Model Reasoning in Robotic Object-Centered Planning},
author = {Moncada-Ramirez, Jesús and Matez-Bandera, J. L and Ruiz-Sarmiento, J. R. and Gonzalez-Jimenez, Javier},
url = {https://www.mdpi.com/2218-6581/14/3/24},
doi = {10.3390/robotics14030024},
issn = {2218-6581},
year  = {2025},
date = {2025-01-01},
journal = {Robotics},
volume = {14},
number = {3},
pages = {24},
abstract = {Large Language Models (LLMs) provide cognitive capabilities that enable robots to interpret and reason about their workspace, especially when paired with semantically rich representations like semantic maps. However, these models are prone to generating inaccurate or invented responses, known as hallucinations, that can produce an erratic robotic operation. This can be addressed by employing agentic workflows, structured processes that guide and refine the model’s output to improve response quality. This work formally defines and qualitatively analyzes the impact of three agentic workflows (LLM Ensemble, Self-Reflection, and Multi-Agent Reflection) on enhancing the reasoning capabilities of an LLM guiding a robotic system to perform object-centered planning. In this context, the LLM is provided with a pre-built semantic map of the environment and a query, to which it must respond by determining the most relevant objects for the query. This response can be used in a multitude of downstream tasks. Extensive experiments were carried out employing state-of-the-art LLMs and semantic maps generated from the widely-used datasets ScanNet and SceneNN. The results show that agentic workflows significantly enhance object retrieval performance, especially in scenarios requiring complex reasoning, with improvements averaging up to 10% over the baseline.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{matez2025_cross,
title = {Cross-Detector Visual Localization with Coplanarity Constraints for Indoor Environments},
author = {Matez-Bandera, J. L and Jaenal, Alberto and Gómez, Clara and C. Hernandez, Alejandra and Monroy, Javier and Araújo, José and Gonzalez-Jimenez, Javier},
url = {https://doi.org/10.3390/s25247593},
doi = {10.3390/s25247593},
issn = {1424-8220},
year  = {2025},
date = {2025-01-01},
journal = {MDPI Sensors},
volume = {25},
number = {24},
pages = {7593},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{matez2022_sigmafp,
title = {Sigma-FP: Robot Mapping of 3D Floor Plans with an RGB-D Camera under Uncertainty},
author = {Matez-Bandera, J. L and Monroy, Javier and Gonzalez-Jimenez, Javier},
url = {https://mapir.isa.uma.es/papersrepo/2022/2022_matez_RAL_sigmaFP.pdf},
doi = {10.1109/LRA.2022.3220156},
issn = {2377-3766},
year  = {2022},
date = {2022-11-01},
journal = {IEEE Robotics and Automation Letters},
volume = {7},
number = {4},
pages = {12539-12546},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{matez_mdpi_2022,
title = {LTC-Mapping, Enhancing Long-term Consistency of Object-oriented Semantic Maps in Robotics},
author = {Matez-Bandera, J. L and Fernandez-Chaves, David and Ruiz-Sarmiento, J. R. and Monroy, Javier and Petkov, Nicolai and Gonzalez-Jimenez, Javier},
url = {https://mapir.isa.uma.es/papersrepo/2022/2022_matez_LTCmapping.pdf},
doi = {10.3390/s22145308},
issn = {1424-8220},
year  = {2022},
date = {2022-01-01},
journal = {MDPI Sensors},
volume = {22},
number = {14},
pages = {5308},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{matez2021,
title = {Efficient semantic place categorization by a robot through active line-of-sight selection},
author = {Matez-Bandera, J. L and Monroy, Javier and Gonzalez-Jimenez, Javier},
url = {https://mapir.isa.uma.es/papersrepo/2021/2021_matez_KBS_semantic_place_categorization.pdf},
doi = {10.1016/j.knosys.2021.108022},
issn = {0950-7051},
year  = {2021},
date = {2021-12-01},
journal = {Knowledge-Based Systems},
volume = {240},
pages = {108022},
abstract = {In this paper, we present an attention mechanism for mobile robots to face the problem of place categorization. Our approach, which is based on active perception, aims to capture images with characteristic or distinctive details of the environment that can be exploited to improve the efficiency (quickness and accuracy) of the place categorization. To do so, at each time moment, our proposal selects the most informative view by controlling the line-of-sight of the robot’s camera through a pan-only unit. We root our proposal on an information maximization scheme, formalized as a next-best-view problem through a Markov Decision Process (MDP) model. The latter exploits the short-time estimated navigation path of the robot to anticipate the next robot’s movements and make consistent decisions. We demonstrate over two datasets, with simulated and real data, that our proposal generalizes well for the two main paradigms of place categorization (object-based and image-based), outperforming typical camera-configurations (fixed and continuously-rotating) and a pure-exploratory approach, both in quickness and accuracy.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}