Dr. Alberto Jaenal Gálvez
#1 Valley #2 Cliff #3 Alp #4 Garfish #5 Capra ibex
ajg@uma.es
Phone
+34 952133361
Address
Lab 2.3.7 E.T.S.I. Informatica – Telecomunicacion, Universidad de Malaga, Campus Universitario de Teatinos, 29071 Malaga, Spain
Dpto. Ingenieria de Sistemas y Automatica
My name is Alberto Jaenal Gálvez, I was born in Málaga (Spain) in 1995. At the present, I am a Ph.D. student at the University of Malaga (Spain) and a researcher in the Machine Perception and Intelligent Robotics group (MAPIR). I am interested in the computer vision research, specifically:
- Appearance-Based Localization
- Deep Learning (Place Recognition, Holistic Descriptors)
- Visual Odometry, SLAM, Optical Flow
Contents
Short bio
I have been doing research since October 2016 when I joined the MAPIR group as a M.Sc. student. I finished my Degree in Electronics, Robotics and Mechatronics (a general engineering which covers mechanics, electronics, control systems, computer science, etc.) in 2016 and my Master in Mechatronics in 2017, both at the University of Malaga. Since October 2018 I hold a FPU grant from the Spanish government to pursue a PhD in Computer Vision under the supervision of Prof. Javier Gonzalez-Jimenez.
I am also a musician, I play the violin since I was 7 and I sing, having participated at diverse regional young orchestras and choirs, among other activities.
Journals (7)
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},
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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. Jaenal, F.A. Moreno, J. Gonzalez-Jimenez
Sequential Monte Carlo localization in topometric appearance maps
The International Journal of Robotics Research, 2023. Q1 · T1
@article{2023_jaenal_SMC_IJRR,
title = {Sequential Monte Carlo localization in topometric appearance maps},
author = {Jaenal, Alberto and Moreno, Francisco-Angel and Gonzalez-Jimenez, Javier},
doi = {10.1177/02783649231197723},
year = {2023},
date = {2023-09-01},
journal = {The International Journal of Robotics Research},
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pubstate = {published},
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2022
A. Jaenal, F.A. Moreno, J. Gonzalez-Jimenez
Unsupervised Appearance Map Abstraction for Indoor Visual Place Recognition With Mobile Robots
IEEE Robotics and Automation Letters, vol. 7, no. 3, pp. 8495-8501, 2022, ISSN: 2377-3766.
@article{jaenal2022unsupervised,
title = {Unsupervised Appearance Map Abstraction for Indoor Visual Place Recognition With Mobile Robots},
author = {Jaenal, Alberto and Moreno, Francisco-Angel and Gonzalez-Jimenez, Javier},
doi = {10.1109/LRA.2022.3186768},
issn = {2377-3766},
year = {2022},
date = {2022-07-01},
journal = {IEEE Robotics and Automation Letters},
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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},
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2021
A. Jaenal, F.A. Moreno, J. Gonzalez-Jimenez
MDPI Sensors, vol. 21, no. 7, pp. 2483, 2021, ISSN: 1424-8220. Q1 · T1
@article{jaenal2021patchwise,
title = {Appearance-Based Sequential Robot Localization Using a Patchwise Approximation of a Descriptor Manifold},
author = {Jaenal, Alberto and Moreno, Francisco-Angel and Gonzalez-Jimenez, Javier},
url = {https://www.mdpi.com/1424-8220/21/7/2483},
doi = {10.3390/s21072483},
issn = {1424-8220},
year = {2021},
date = {2021-04-01},
journal = {MDPI Sensors},
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2020
D. Zuñiga-Noël, A. Jaenal, R. Gomez-Ojeda, J. Gonzalez-Jimenez
The UMA-VI dataset: Visual–inertial odometry in low-textured and dynamic illumination environments
The International Journal of Robotics Research, 2020.
@article{zuniga2020ijrr,
title = {The UMA-VI dataset: Visual–inertial odometry in low-textured and dynamic illumination environments},
author = {Zuñiga-Noël, David and Jaenal, Alberto and Gomez-Ojeda, Ruben and Gonzalez-Jimenez, Javier},
url = {https://mapir.isa.uma.es/papersrepo/2020/2020_The_UMA-VI_dataset_Visual%e2%80%93inertial_odometry_in_low-textured_and_dynamic_illumination_environments.pdf
https://journals.sagepub.com/doi/10.1177/0278364920938439},
doi = {10.1177/0278364920938439},
year = {2020},
date = {2020-07-01},
journal = {The International Journal of Robotics Research},
abstract = {This article presents a visual–inertial dataset gathered in indoor and outdoor scenarios with a handheld custom sensor rig, for over 80 min in total. The dataset contains hardware-synchronized data from a commercial stereo camera (Bumblebee®2), a custom stereo rig, and an inertial measurement unit. The most distinctive feature of this dataset is the strong presence of low-textured environments and scenes with dynamic illumination, which are recurrent corner cases of visual odometry and simultaneous localization and mapping (SLAM) methods. The dataset comprises 32 sequences and is provided with ground-truth poses at the beginning and the end of each of the sequences, thus allowing the accumulated drift to be measured in each case. We provide a trial evaluation of five existing state-of-the-art visual and visual–inertial methods on a subset of the dataset. We also make available open-source tools for evaluation purposes, as well as the intrinsic and extrinsic calibration parameters of all sensors in the rig. The dataset is available for download at https://mapir.isa.uma.es/work/uma-visual-inertial-dataset},
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This article presents a visual–inertial dataset gathered in indoor and outdoor scenarios with a handheld custom sensor rig, for over 80 min in total. The dataset contains hardware-synchronized data from a commercial stereo camera (Bumblebee®2), a custom stereo rig, and an inertial measurement unit. The most distinctive feature of this dataset is the strong presence of low-textured environments and scenes with dynamic illumination, which are recurrent corner cases of visual odometry and simultaneous localization and mapping (SLAM) methods. The dataset comprises 32 sequences and is provided with ground-truth poses at the beginning and the end of each of the sequences, thus allowing the accumulated drift to be measured in each case. We provide a trial evaluation of five existing state-of-the-art visual and visual–inertial methods on a subset of the dataset. We also make available open-source tools for evaluation purposes, as well as the intrinsic and extrinsic calibration parameters of all sensors in the rig. The dataset is available for download at https://mapir.isa.uma.es/work/uma-visual-inertial-dataset
Book Chapters (2)
2021
A. Jaenal, F.A. Moreno, J. Gonzalez-Jimenez
Experimental Analysis of Appearance Maps as Descriptor Manifolds Approximations
Computer Analysis of Images and Patterns, pp. 109–119, Springer International Publishing, Cham, 2021, ISBN: 978-3-030-89131-2.
@inbook{jaenal2021appmanifolds,
title = {Experimental Analysis of Appearance Maps as Descriptor Manifolds Approximations},
author = {Jaenal, Alberto and Moreno, Francisco-Angel and Gonzalez-Jimenez, Javier},
doi = {10.1007/978-3-030-89131-2_10},
isbn = {978-3-030-89131-2},
year = {2021},
date = {2021-01-01},
booktitle = {Computer Analysis of Images and Patterns},
pages = {109--119},
publisher = {Springer International Publishing},
address = {Cham},
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2018
A. Gongora, D. Fernandez-Chaves, A. Jaenal, J. Monroy, J. Gonzalez-Jimenez
Petkov, Nicolai; Strisciuglio, Nicola; Travieso-González, Carlos M. (Ed.): Frontiers in Artificial Intelligence and Applications (FAIA), IOS Press, pp. 134–145, IOS Press, 2018.
@inbook{gongora_faia18,
title = {Toward the Generation of Smell Maps: Matching Electro-Chemical Sensor Information with Human Odor Perception},
author = {Gongora, Andres and Fernandez-Chaves, David and Jaenal, Alberto and Monroy, Javier and Gonzalez-Jimenez, Javier},
editor = {Petkov, Nicolai and Strisciuglio, Nicola and Travieso-González, Carlos M.},
url = {https://mapir.isa.uma.es/papersrepo/2018/2018_gongora_FAIA_Toward the Generation of Smell Maps.pdf},
doi = {10.3233/978-1-61499-929-4-134},
year = {2018},
date = {2018-01-01},
booktitle = {Frontiers in Artificial Intelligence and Applications (FAIA), IOS Press},
pages = {134--145},
publisher = {IOS Press},
keywords = {},
pubstate = {published},
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Conferences (4)
2020
A. Jaenal, D. Zuñiga-Noël, R. Gomez-Ojeda, J. Gonzalez-Jimenez
Improving Visual SLAM in Car-Navigated Urban Environments with Appearance Maps
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4679-4685, 2020.
@inproceedings{jaenal2021improving,
title = {Improving Visual SLAM in Car-Navigated Urban Environments with Appearance Maps},
author = {Jaenal, Alberto and Zuñiga-Noël, David and Gomez-Ojeda, Ruben and Gonzalez-Jimenez, Javier},
doi = {10.1109/IROS45743.2020.9341451},
year = {2020},
date = {2020-01-01},
booktitle = {2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
pages = {4679-4685},
abstract = {This paper describes a method that corrects errors of a VSLAM-estimated trajectory for cars driving in GPS-denied environments, by applying constraints from public databases of geo-tagged images (Google Street View, Mapillary, etc). The method, dubbed Appearance-based Geo-Alignment for Simultaneous Localisation and Mapping (AGA-SLAM), encodes the available image database as an appearance map, which represents the space with a compact holistic descriptor for each image plus its associated geo-tag. The VSLAM trajectory is corrected on-line by incorporating constraints from the recognized places along the trajectory into a position-based optimization framework. The paper presents a seamless formulation to combine local and absolute metric observations with associations from Visual Place Recognition. The robustness of the holistic image descriptor to changes due to weather or illumination variations ensures a long-term consistent method to improve car localization. The proposed method has been extensively evaluated on more than 70 sequences from 4 different datasets, proving out its effectiveness and endurance to appearance challenges.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
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This paper describes a method that corrects errors of a VSLAM-estimated trajectory for cars driving in GPS-denied environments, by applying constraints from public databases of geo-tagged images (Google Street View, Mapillary, etc). The method, dubbed Appearance-based Geo-Alignment for Simultaneous Localisation and Mapping (AGA-SLAM), encodes the available image database as an appearance map, which represents the space with a compact holistic descriptor for each image plus its associated geo-tag. The VSLAM trajectory is corrected on-line by incorporating constraints from the recognized places along the trajectory into a position-based optimization framework. The paper presents a seamless formulation to combine local and absolute metric observations with associations from Visual Place Recognition. The robustness of the holistic image descriptor to changes due to weather or illumination variations ensures a long-term consistent method to improve car localization. The proposed method has been extensively evaluated on more than 70 sequences from 4 different datasets, proving out its effectiveness and endurance to appearance challenges.
2019
A. Jaenal, F.A. Moreno, J. Gonzalez-Jimenez
Proceedings of the 2Nd International Conference on Applications of Intelligent Systems, pp. 28:1–28:6, ACM, New York, NY, USA, 2019, ISBN: 978-1-4503-6085-2.
@inproceedings{jaenal2019,
title = {Experimental Study of the Suitability of CNN-based Holistic Descriptors for Accurate Visual Localization},
author = {Jaenal, Alberto and Moreno, Francisco-Angel and Gonzalez-Jimenez, Javier},
url = {https://mapir.isa.uma.es/papersrepo/2019/2019_jaenal_APPIS_A_good_holistic_image_feature_for_localization.pdf
http://doi.acm.org/10.1145/3309772.3309800},
doi = {10.1145/3309772.3309800},
isbn = {978-1-4503-6085-2},
year = {2019},
date = {2019-01-01},
booktitle = {Proceedings of the 2Nd International Conference on Applications of Intelligent Systems},
pages = {28:1--28:6},
publisher = {ACM},
address = {New York, NY, USA},
series = {APPIS '19},
abstract = {Holistic Image Descriptors (HIDs) are compact representations of a whole image that, being suitable for Place Recognition, are not appropriate for accurate Visual Localization. The most successful HIDs are those extracted from Convolutional Neural Networks (CNNs) like VGG, ResNet, InceptionV4 or NetVLAD. Very recently, the equivariance property has been proposed to reflect how image 2D transformations (e.g. rotation, flip, scale changes) influence the descriptor [17]. Our work experimentally analyzes whether such property can be a good indicator of the suitability of the existing CNN-based HID for estimating changes in the camera pose, which produces more complex transformations of the image than the pure transformations analyzed in [17]. The results we report here are a preliminary work in the context of an ongoing project towards appearance-based localization of autonomous mobile robots.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
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Holistic Image Descriptors (HIDs) are compact representations of a whole image that, being suitable for Place Recognition, are not appropriate for accurate Visual Localization. The most successful HIDs are those extracted from Convolutional Neural Networks (CNNs) like VGG, ResNet, InceptionV4 or NetVLAD. Very recently, the equivariance property has been proposed to reflect how image 2D transformations (e.g. rotation, flip, scale changes) influence the descriptor [17]. Our work experimentally analyzes whether such property can be a good indicator of the suitability of the existing CNN-based HID for estimating changes in the camera pose, which produces more complex transformations of the image than the pure transformations analyzed in [17]. The results we report here are a preliminary work in the context of an ongoing project towards appearance-based localization of autonomous mobile robots.
A. Gongora, A. Jaenal, D. Fernandez-Chaves, J. Monroy, J. Gonzalez-Jimenez
Urban Monitoring of Unpleasant Odors with a Handheld Electronic Nose
ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN), Fukuoka (Japan), 2019.
@inproceedings{gongora_ISOEN_2019,
title = {Urban Monitoring of Unpleasant Odors with a Handheld Electronic Nose},
author = {Gongora, Andres and Jaenal, Alberto and Fernandez-Chaves, David and Monroy, Javier and Gonzalez-Jimenez, Javier},
url = {https://mapir.isa.uma.es/papersrepo/2019/2019_gongora_ISOEN_Monitoring_Phenol_in_Coria_del_Rio.pdf},
doi = {10.1109/ISOEN.2019.8823219},
year = {2019},
date = {2019-01-01},
booktitle = {ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN)},
address = {Fukuoka (Japan)},
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2018
A. Gongora, D. Fernandez-Chaves, A. Jaenal, J. Monroy, J. Gonzalez-Jimenez
International Conference on Applications of Intelligent Systems (APPIS), Las Palmas de Gran Canaria (Spain), 2018.
@inproceedings{gongora2018b,
title = {Toward the Generation of Smell Maps: Matching Electro-Chemical Sensor Information with Human Odor Perception},
author = {Gongora, Andres and Fernandez-Chaves, David and Jaenal, Alberto and Monroy, Javier and Gonzalez-Jimenez, Javier},
url = {https://mapir.isa.uma.es/papersrepo/2018/2018_gongora_APPIS_Toward the Generation of Smell Maps.pdf},
year = {2018},
date = {2018-01-01},
booktitle = {International Conference on Applications of Intelligent Systems (APPIS)},
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National conferences (1)
2022
S. Luengo, A. Jaenal, F.A. Moreno, J. Gonzalez-Jimenez
Dimensionality Reduction in images for Appearance-based camera Localization
XLIII Jornadas de Automática, pp. 721-727, CEA -- Comité Español de Automática + Universidad de La Rioja CEA -- Comité Español de Automática, Logroño, Spain, 2022.
@inproceedings{2022_JJAA_luengo_DRI,
title = {Dimensionality Reduction in images for Appearance-based camera Localization},
author = {Luengo, Silvia and Jaenal, Alberto and Moreno, Francisco-Angel and Gonzalez-Jimenez, Javier},
url = {https://doi.org/10.17979/spudc.9788497498418},
doi = {10.17979/spudc.9788497498418},
year = {2022},
date = {2022-09-01},
booktitle = {XLIII Jornadas de Automática},
pages = {721-727},
publisher = {CEA -- Comité Español de Automática},
address = {Logroño, Spain},
organization = {CEA -- Comité Español de Automática + Universidad de La Rioja},
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PhD Theses (1)
2023
A. Jaenal, F.A. Moreno, J. Gonzalez-Jimenez
University of Málaga, 2023.
@phdthesis{jaenal2023,
title = {Appearance-based Localization},
author = {Jaenal, Alberto and Moreno, Francisco-Angel and Gonzalez-Jimenez, Javier},
url = {https://hdl.handle.net/10630/30592},
year = {2023},
date = {2023-01-01},
school = {University of Málaga},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}
