ARPEGGIO: Advanced Robotic Perception for Mapping and Localization(Sep’21–Feb’25)
National Project
National Project
Mapping and localization (M&L) are two fundamental pillars to achieve effective, intelligent mobile robots. Though conceptually these two problems are now well understood, and good realizations are available to the robotics community, critical issues are still to be solved to reach the degree of robustness and dependability that real applications demand. This project will investigate improvements of existing methods as well as novel approaches toward this aim from both the geometric and the semantic perspectives. At the geometric level, we will investigate:
- Appearance-based M&L, a challenging approach that models the environment with a collection of geo-tagged holistic image descriptors from which localization is performed through the regression of a query image descriptor with a subset of such map.
- Direct, dense registration methods to exploit the new generation of digital and solid-state 3D Lidars.
- Mathematical tools to guarantee the optimality of the solutions computed at the back-end of a variety of M&L subproblems (relative pose between cameras, 3D point registration, or graph-pose optimization).
As for the semantic standpoint, the robot will adopt an active vision strategy to gain effectiveness and efficiency when building semantic models of the robot environment.
REFERENCE: PID2020-117057GB-I00
FUNDED BY: Ministerio de Ciencia e Innovación
PERIOD: Sep 2021 – Aug 2024 (extended to Feb 2025)
PRINCIPAL RESEARCHER: JAVIER GONZÁLEZ-JIMÉNEZ
INSTITUTION: Unversity of Málaga
Publications
Publications in the scope of this project:
Contents
Journals (14)
2026
J.L. Matez-Bandera, P. Ojeda, J. Monroy, J. Gonzalez-Jimenez, J.R. Ruiz-Sarmiento
Robotics and Autonomous Systems, vol. 205, pp. 105668, 2026. Q1 · T1
@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},
year = {2026},
date = {2026-01-01},
urldate = {2026-01-01},
journal = {Robotics and Autonomous Systems},
volume = {205},
pages = {105668},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2025
J.L. Matez-Bandera, A. Jaenal, C. Gómez, A. C. Hernandez, J. Monroy, J. Araújo, J. Gonzalez-Jimenez
Cross-Detector Visual Localization with Coplanarity Constraints for Indoor Environments
MDPI Sensors, 2025. Q2 · T1
@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},
year = {2025},
date = {2025-01-01},
journal = {MDPI Sensors},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2024
A. Jaenal, J.R. Ruiz-Sarmiento, J. Gonzalez-Jimenez
Engineering Applications of Artificial Intelligence, vol. 127, pp. 107365, 2024, ISSN: 0952-1976. Q1 · T1
@article{alberto2024eaai,
title = {MachNet, a general Deep Learning architecture for Predictive Maintenance within the industry 4.0 paradigm},
author = {Jaenal, Alberto and Ruiz-Sarmiento, J. R. and Gonzalez-Jimenez, Javier},
url = {https://www.sciencedirect.com/science/article/pii/S095219762301549X},
doi = {10.1016/j.engappai.2023.107365},
issn = {0952-1976},
year = {2024},
date = {2024-01-01},
journal = {Engineering Applications of Artificial Intelligence},
volume = {127},
pages = {107365},
abstract = {In the Industry 4.0 era, a myriad of sensors of diverse nature (temperature, pressure, etc.) is spreading throughout the entire value chain of industries, being potentially exploitable for multiple purposes, such as Predictive Maintenance (PdM): the just-in-time maintenance of industrial assets, which results in reduced operating costs, increased operator safety, etc. Nowadays, industrial processes require to be highly configurable, in order to proactively adapt their operation to diverse factors such as user needs, product updates or supply chain uncertainties. This limits current Industry 4.0-PdM solutions, typically consisting of ad-hoc developments intended for specific scenarios, i.e. they are designed to operate under certain conditions (configurations, employed sensors, etc.), being unable to manage changes in their setup. This paper presents a general Deep Learning (DL) architecture, MachNet, which deals with such heterogeneity and is able to address PdM problems of a diverse nature. The modularity of the proposed architecture enables it to deal with an arbitrary number of sensors of different types, also allowing the integration of prior information (age of assets, material type, etc.), which clearly affects performance and is often neglected. In practice, our architecture effortlessly adapts to the assets’ specifications and to different PdM problems. That is, MachNet becomes an architectural template that can be instantiated for a given scenario. We tested our proposal in two different PdM-related problems: Health State (HS) and Remaining-useful-Life (RuL) estimation, achieving in both cases comparable or superior performance to other state-of-the-art approaches, with the additional advantage of the generality that MachNet offers.},
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In the Industry 4.0 era, a myriad of sensors of diverse nature (temperature, pressure, etc.) is spreading throughout the entire value chain of industries, being potentially exploitable for multiple purposes, such as Predictive Maintenance (PdM): the just-in-time maintenance of industrial assets, which results in reduced operating costs, increased operator safety, etc. Nowadays, industrial processes require to be highly configurable, in order to proactively adapt their operation to diverse factors such as user needs, product updates or supply chain uncertainties. This limits current Industry 4.0-PdM solutions, typically consisting of ad-hoc developments intended for specific scenarios, i.e. they are designed to operate under certain conditions (configurations, employed sensors, etc.), being unable to manage changes in their setup. This paper presents a general Deep Learning (DL) architecture, MachNet, which deals with such heterogeneity and is able to address PdM problems of a diverse nature. The modularity of the proposed architecture enables it to deal with an arbitrary number of sensors of different types, also allowing the integration of prior information (age of assets, material type, etc.), which clearly affects performance and is often neglected. In practice, our architecture effortlessly adapts to the assets’ specifications and to different PdM problems. That is, MachNet becomes an architectural template that can be instantiated for a given scenario. We tested our proposal in two different PdM-related problems: Health State (HS) and Remaining-useful-Life (RuL) estimation, achieving in both cases comparable or superior performance to other state-of-the-art approaches, with the additional advantage of the generality that MachNet offers.
2023
A. Galeote-Luque, J.R. Ruiz-Sarmiento, J. Gonzalez-Jimenez
GND-LO: Ground Decoupled 3D Lidar Odometry Based on Planar Patches
IEEE Robotics and Automation Letters, vol. 8, no. 11, pp. 6923 - 6930, 2023. Q2 · T1
@article{galeoteluque2023,
title = {GND-LO: Ground Decoupled 3D Lidar Odometry Based on Planar Patches},
author = {Galeote-Luque, Andres and Ruiz-Sarmiento, J. R. and Gonzalez-Jimenez, Javier},
url = {https://ieeexplore.ieee.org/abstract/document/10243099},
doi = {10.1109/LRA.2023.3313057},
year = {2023},
date = {2023-09-01},
journal = {IEEE Robotics and Automation Letters},
volume = {8},
number = {11},
pages = {6923 - 6930},
abstract = {We present a new fast Ground Decoupled 3D Lidar Odometry (GND-LO) method. The particularity of GND-LO is that it takes advantage of the distinct spatial layout found in urban settings to efficiently recover the lidar movement in a decoupled manner. For that, the input scans are reduced to a set of planar patches extracted from the flat surfaces of the scene, found aplenty in these scenarios. These patches can be labeled as either belonging to the ground or walls, decoupling the estimation into two steps. First, the ground planes from each scan, clustered from the ground patches, are registered. Then, the motion estimation is completed by minimizing the distance between the wall patches and their corresponding points from the other scan, whose pairing is iteratively updated. GND-LO has demonstrated to perform both precisely and efficiently beating state-of-the-art approaches. Concretely, experiments on the popular KITTI dataset show that our proposal outperforms its competitors by reducing the average drift by 19% in translation and 4% in rotation. This is achieved by running in real-time without needing GPU or optimized multi-threading, as it is commonplace in the literature.},
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We present a new fast Ground Decoupled 3D Lidar Odometry (GND-LO) method. The particularity of GND-LO is that it takes advantage of the distinct spatial layout found in urban settings to efficiently recover the lidar movement in a decoupled manner. For that, the input scans are reduced to a set of planar patches extracted from the flat surfaces of the scene, found aplenty in these scenarios. These patches can be labeled as either belonging to the ground or walls, decoupling the estimation into two steps. First, the ground planes from each scan, clustered from the ground patches, are registered. Then, the motion estimation is completed by minimizing the distance between the wall patches and their corresponding points from the other scan, whose pairing is iteratively updated. GND-LO has demonstrated to perform both precisely and efficiently beating state-of-the-art approaches. Concretely, experiments on the popular KITTI dataset show that our proposal outperforms its competitors by reducing the average drift by 19% in translation and 4% in rotation. This is achieved by running in real-time without needing GPU or optimized multi-threading, as it is commonplace in the literature.
G. Ambrosio, J.R. Ruiz-Sarmiento, J. Gonzalez-Jimenez
The Robot@Home2 dataset: A new release with improved usability tools
SoftwareX, vol. 23, pp. 101490, 2023, ISSN: 2352-7110. Q2 · T2
@article{goyo2023softwarex,
title = {The Robot@Home2 dataset: A new release with improved usability tools},
author = {Ambrosio, Gregorio and Ruiz-Sarmiento, J. R. and Gonzalez-Jimenez, Javier},
doi = {10.1016/j.softx.2023.101490},
issn = {2352-7110},
year = {2023},
date = {2023-07-01},
journal = {SoftwareX},
volume = {23},
pages = {101490},
abstract = {Released in 2017, Robot@Home is a dataset captured by a mobile robot during indoor navigation sessions in apartments. This paper presents Robot@Home2, an enhanced version of the Robot@Home dataset, aimed at improving usability and functionality for developing and testing mobile robotics and computer vision algorithms. Robot@Home2 consists of three main components. Firstly, a relational database that states the contextual information and data links, compatible with Standard Query Language. Secondly,a Python package for managing the database, including downloading, querying, and interfacing functions. Finally, learning resources in the form of Jupyter notebooks, runnable locally or on the Google Colab platform, enabling users to explore the dataset without local installations. These freely available tools are expected to enhance the ease of exploiting the Robot@Home dataset and accelerate research in computer vision and robotics.},
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Released in 2017, Robot@Home is a dataset captured by a mobile robot during indoor navigation sessions in apartments. This paper presents Robot@Home2, an enhanced version of the Robot@Home dataset, aimed at improving usability and functionality for developing and testing mobile robotics and computer vision algorithms. Robot@Home2 consists of three main components. Firstly, a relational database that states the contextual information and data links, compatible with Standard Query Language. Secondly,a Python package for managing the database, including downloading, querying, and interfacing functions. Finally, learning resources in the form of Jupyter notebooks, runnable locally or on the Google Colab platform, enabling users to explore the dataset without local installations. These freely available tools are expected to enhance the ease of exploiting the Robot@Home dataset and accelerate research in computer vision and robotics.
M. Garcia-Salguero, J. Gonzalez-Jimenez
Certifiable solver for real-time N-view triangulation
IEEE Robotics and Automation Letters, vol. 8, no. 4, pp. 1999-2005, 2023, ISSN: 2377-3766. Q2 · T1
@article{garciasalguero2023b,
title = {Certifiable solver for real-time N-view triangulation},
author = {Garcia-Salguero, Mercedes and Gonzalez-Jimenez, Javier},
url = {https://mapir.isa.uma.es/papersrepo/2023/2023_mercedes_RAL_Nview_triangulation_paper.pdf},
doi = {10.1109/LRA.2023.3245408},
issn = {2377-3766},
year = {2023},
date = {2023-04-01},
journal = {IEEE Robotics and Automation Letters},
volume = {8},
number = {4},
pages = {1999-2005},
keywords = {},
pubstate = {published},
tppubtype = {article}
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M. Garcia-Salguero, J. Gonzalez-Jimenez
Fast certifiable relative pose estimation with gravity prior
Artificial Intelligence, 2023. Q1 · T1
@article{garciasalguero2023,
title = {Fast certifiable relative pose estimation with gravity prior},
author = {Garcia-Salguero, Mercedes and Gonzalez-Jimenez, Javier},
url = {https://mapir.isa.uma.es/papersrepo/2023/2023_mercedes_AI_priorRPp_doc.pdf},
doi = {10.1016/j.artint.2023.103862},
year = {2023},
date = {2023-01-01},
journal = {Artificial Intelligence},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2022
J.L. Matez-Bandera, J. Monroy, J. Gonzalez-Jimenez
Sigma-FP: Robot Mapping of 3D Floor Plans with an RGB-D Camera under Uncertainty
IEEE Robotics and Automation Letters, 2022. Q2 · T2
@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},
year = {2022},
date = {2022-11-01},
journal = {IEEE Robotics and Automation Letters},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
M. Garcia-Salguero, J. Gonzalez-Jimenez
Certifiable algorithms for the two-view planar triangulation problem
Computer Vision and Image Understanding, 2022. Q2 · T2
@article{garciasalguero2022b,
title = {Certifiable algorithms for the two-view planar triangulation problem},
author = {Garcia-Salguero, Mercedes and Gonzalez-Jimenez, Javier},
url = {https://mapir.isa.uma.es/papersrepo/2022/2022_mercedes_CVIU_2view_planar_triangulation.pdf},
doi = {10.1016/j.cviu.2022.103570},
year = {2022},
date = {2022-10-01},
journal = {Computer Vision and Image Understanding},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
J.L. Matez-Bandera, D. Fernandez-Chaves, J.R. Ruiz-Sarmiento, J. Monroy, N. Petkov, J. Gonzalez-Jimenez
LTC-Mapping, Enhancing Long-term Consistency of Object-oriented Semantic Maps in Robotics
MDPI Sensors, vol. 22, no. 14, 2022, ISSN: 1424-8220. Q2 · T2
@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},
keywords = {},
pubstate = {published},
tppubtype = {article}
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D. Fernandez-Chaves, J.R. Ruiz-Sarmiento, A. Jaenal, N. Petkov, J. Gonzalez-Jimenez
Expert Systems with Applications, vol. 208, pp. 117970, 2022, ISSN: 0957-4174. Q1 · T1
@article{fernandez2022eswa,
title = {Robot@VirtualHome, an ecosystem of virtual environments and tools for realistic indoor robotic simulation},
author = {Fernandez-Chaves, David and Ruiz-Sarmiento, J. R. and Jaenal, Alberto and Petkov, Nicolai and Gonzalez-Jimenez, Javier},
url = {https://www.sciencedirect.com/science/article/pii/S0957417422012015},
doi = {10.1016/j.eswa.2022.117970},
issn = {0957-4174},
year = {2022},
date = {2022-01-01},
journal = {Expert Systems with Applications},
volume = {208},
pages = {117970},
keywords = {},
pubstate = {published},
tppubtype = {article}
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2021
J.L. Matez-Bandera, J. Monroy, J. Gonzalez-Jimenez
Efficient semantic place categorization by a robot through active line-of-sight selection
Knowledge-Based Systems, vol. 240, 2021, ISSN: 0950-7051. Q1 · T1
@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},
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.},
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pubstate = {published},
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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.
P. Ojeda, J. Monroy, J. Gonzalez-Jimenez
A Simulation Framework for the Integration of Artificial Olfaction into Multi-Sensor Mobile Robots
MDPI Sensors, vol. 21, no. 6, pp. 2041, 2021, ISSN: 1424-8220. Q2 · T1
@article{ojeda_2021_sensors,
title = {A Simulation Framework for the Integration of Artificial Olfaction into Multi-Sensor Mobile Robots},
author = {Ojeda, Pepe and Monroy, Javier and Gonzalez-Jimenez, Javier},
url = {https://mapir.isa.uma.es/papersrepo/2021/2021_ojeda_sensors_gaden_unity.pdf},
doi = {10.3390/s21062041},
issn = {1424-8220},
year = {2021},
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P. Ojeda, J. Monroy, J. Gonzalez-Jimenez
IEEE Robotics and Automation Letters (RAL), vol. 6, no. 2, pp. 1320-1326, 2021. Q2 · T2
@article{ojeda_2021_ral,
title = {Information-driven Gas Source Localization Exploiting Gas and Wind Local Measurements for Autonomous Mobile Robots},
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doi = {10.1109/LRA.2021.3057290},
year = {2021},
date = {2021-01-01},
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number = {2},
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Book Chapters (1)
2021
A.M. Burgueño, J.R. Ruiz-Sarmiento, J. Gonzalez-Jimenez
Autonomous Docking of Mobile Robots by Reinforcement Learning Tackling the Sparse Reward Problem
vol. 12862, Chapter Advances in Computational Intelligence, pp. 392-403, Springer International Publishing, Cham, 2021, ISBN: 978-3-030-85099-9.
@inbook{burgeno2021docking,
title = {Autonomous Docking of Mobile Robots by Reinforcement Learning Tackling the Sparse Reward Problem},
author = {Burgueño, A. M. and Ruiz-Sarmiento, J. R. and Gonzalez-Jimenez, Javier},
url = {https://mapir.isa.uma.es/papersrepo/2021/2021_burgeno_IWAN_Autonomous_docking_of_mobile_robots.pdf},
doi = {10.1007/978-3-030-85099-9_32},
isbn = {978-3-030-85099-9},
year = {2021},
date = {2021-01-01},
volume = {12862},
pages = {392-403},
publisher = {Springer International Publishing},
address = {Cham},
chapter = {Advances in Computational Intelligence},
series = {Lecture Notes in Computer Science},
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Conferences (3)
2023
M. Luperto, J.L. Matez-Bandera, T. Piotr Kucner, M. Antonazzi, G. Somaschini, J. Monroy, J. Gonzalez-Jimenez, N. Basilico
Ensuring the Consistency of Heterogeneous World Representations Using Structural Features
International Conference on Robotics and Automation (ICRA), 2023.
@inproceedings{luperto_etal_2023_ICRA_ws,
title = {Ensuring the Consistency of Heterogeneous World Representations Using Structural Features},
author = {Luperto, Matteo and Matez-Bandera, J. L and Piotr Kucner, Tomasz and Antonazzi, Michele and Somaschini, Gabriele and Monroy, Javier and Gonzalez-Jimenez, Javier and Basilico, Nicola},
url = {https://mapir.isa.uma.es/papersrepo/2023/20023_Luperto_consitency_mapping.pdf},
year = {2023},
date = {2023-01-01},
booktitle = {International Conference on Robotics and Automation (ICRA)},
series = {Workshop: Unconventional spatial representations: Opportunities for robotics},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
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A.J. Pérez Bazuelo, J.R. Ruiz-Sarmiento, G. Ambrosio, J. Gonzalez-Jimenez
Towards a Voxelized Semantic Representation of the Workspace of Mobile Robots
Advances in Computational Intelligence. IWANN, pp. 194–205, Springer Nature, Switzerland, 2023, ISBN: 978-3-031-43078-7.
@inproceedings{raul2023iwann,
title = {Towards a Voxelized Semantic Representation of the Workspace of Mobile Robots},
author = {Pérez Bazuelo, Antonio Jesús and Ruiz-Sarmiento, J. R. and Ambrosio, Gregorio and Gonzalez-Jimenez, Javier},
doi = {10.1007/978-3-031-43078-7_16},
isbn = {978-3-031-43078-7},
year = {2023},
date = {2023-01-01},
booktitle = {Advances in Computational Intelligence. IWANN},
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pages = {194--205},
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2021
D. Fernandez-Chaves, J.L. Matez-Bandera, J.R. Ruiz-Sarmiento, J. Monroy, N. Petkov, J. Gonzalez-Jimenez
Exploiting Spatio-Temporal Coherence for Video Object Detection in Robotics
International Conference on Computer Analysis of Images and Patterns (CAIP), 2021.
@inproceedings{fernandez_caip_2021,
title = {Exploiting Spatio-Temporal Coherence for Video Object Detection in Robotics},
author = {Fernandez-Chaves, David and Matez-Bandera, J. L and Ruiz-Sarmiento, J. R. and Monroy, Javier and Petkov, Nicolai and Gonzalez-Jimenez, Javier},
url = {https://mapir.isa.uma.es/papersrepo/2021/2021_fernandezchaves_CAIP_espation_temporal_coherence_objects.pdf},
doi = {10.1007/978-3-030-89131-2_17},
year = {2021},
date = {2021-01-01},
booktitle = {International Conference on Computer Analysis of Images and Patterns (CAIP)},
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National conferences (4)
2024
J. Moncada-Ramirez, J.R. Ruiz-Sarmiento, J.L. Matez-Bandera, J. Gonzalez-Jimenez
Large Models for Semantic Mapping in Mobile Robotics
XLV Jornadas de Automática, Málaga (Spain), 2024.
@inproceedings{moncada2024ja,
title = {Large Models for Semantic Mapping in Mobile Robotics},
author = {Moncada-Ramirez, Jesús and Ruiz-Sarmiento, J. R. and Matez-Bandera, J. L and Gonzalez-Jimenez, Javier},
url = {https://revistas.udc.es/index.php/JA_CEA/article/view/10940/8379},
doi = {10.17979/ja-cea.2024.45.10940},
year = {2024},
date = {2024-09-01},
booktitle = {XLV Jornadas de Automática},
address = {Málaga (Spain)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
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G. Ambrosio, J.L. Matez-Bandera, J.R. Ruiz-Sarmiento, J. Gonzalez-Jimenez
Linux Container-based Environment for the Development of Robotic Applications
XLV Jornadas de Automática, Málaga (Spain), 2024.
@inproceedings{goyo2024ja,
title = {Linux Container-based Environment for the Development of Robotic Applications},
author = {Ambrosio, Gregorio and Matez-Bandera, J. L and Ruiz-Sarmiento, J. R. and Gonzalez-Jimenez, Javier},
url = {https://revistas.udc.es/index.php/JA_CEA/article/view/10943/8381},
doi = {10.17979/ja-cea.2024.45.10943},
year = {2024},
date = {2024-09-01},
booktitle = {XLV Jornadas de Automática},
address = {Málaga (Spain)},
keywords = {},
pubstate = {published},
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A. Cañete, E. Quemada-Torres, J.R. Ruiz-Sarmiento, F.A. Moreno, J. Gonzalez-Jimenez
Multimodal System for the Orientation of Mobile Robots Towards its Interlocutor
XLV Jornadas de Automática, Málaga (Spain), 2024.
@inproceedings{ant2024ja,
title = {Multimodal System for the Orientation of Mobile Robots Towards its Interlocutor},
author = {Cañete, A. and Quemada-Torres, E. and Ruiz-Sarmiento, J. R. and Moreno, Francisco-Angel and Gonzalez-Jimenez, Javier},
url = {https://revistas.udc.es/index.php/JA_CEA/article/view/10939/8378},
doi = {10.17979/ja-cea.2024.45.10939},
year = {2024},
date = {2024-09-01},
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booktitle = {XLV Jornadas de Automática},
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P. Ojeda, J. Monroy, J. Gonzalez-Jimenez
Localización de emisiones de metano combinando un sensor TDLAS y un robot móvil
Jornadas Automática CEA, Málaga, 2024.
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title = {Localización de emisiones de metano combinando un sensor TDLAS y un robot móvil},
author = {Ojeda, Pepe and Monroy, Javier and Gonzalez-Jimenez, Javier},
url = {https://revistas.udc.es/index.php/JA_CEA/article/view/10893/8347},
doi = {10.17979/ja-cea.2024.45.10893},
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date = {2024-01-01},
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