Contact
Name | Carlos Medina Sánchez |
---|---|
Position | Researcher |
Phone | +49-201-183-6362 |
Fax | +49-201-183-4176 |
carlos.medina-sanchez@uni-due.de | |
Address | Schützenbahn 70 Building SA 45127 Essen |
Room | SA-328 |

Research
- Multi-Robot deployment
- Mapping
- Social navigation
Education
- Since 10.2018 University of Duisburg-Essen, Ph.D. Student (Networked Embedded Systems, NES)
- 2017 – Master in Electronics, Robotics and Automatics Engineering from Seville University
- 2013 – Mechatronics Engineering from San Buenaventura University
Employments
- Since 10.2018 University of Duisburg-Essen, Researcher (Networked Embedded Systems, NES)
- 2018 – I EAN University, Bogotá, Colombia, Assistant Professor
Bachelor Theses
- Design and construction of a pharmaceutical ampoule washing and drying machine
Master Theses
- Improvement of the path planning of an ASV based on analisys of dissimilarity of the population of a GA
Publications
2020 |
Carlos Medina-Sánchez, Matteo Zella, Jesús Capitán, Pedro José Marrón: Semantic Mapping with Low-Density Point-Clouds for Service Robots in Indoor Environments. Appl. Sci., 10 (7154), 2020. (Type: Journal Article | Abstract | Links) @article{Medina-Sanchez2020-1, title = {Semantic Mapping with Low-Density Point-Clouds for Service Robots in Indoor Environments}, author = {Carlos Medina-Sánchez and Matteo Zella and Jesús Capitán and Pedro José Marrón}, url = {https://www.mdpi.com/2076-3417/10/20/7154}, doi = {https://doi.org/10.3390/app10207154}, year = {2020}, date = {2020-10-14}, journal = {Appl. Sci.}, volume = {10}, number = {7154}, abstract = {The advancements in the robotic field have made it possible for service robots to increasingly become part of everyday indoor scenarios. Their ability to operate and reach defined goals depends on the perception and understanding of their surrounding environment. Detecting and positioning objects as well as people in an accurate semantic map are, therefore, essential tasks that a robot needs to carry out. In this work, we walk an alternative path to build semantic maps of indoor scenarios. Instead of relying on high-density sensory input, like the one provided by an RGB-D camera, and resource-intensive processing algorithms, like the ones based on deep learning, we investigate the use of low-density point-clouds provided by 3D LiDARs together with a set of practical segmentation methods for the detection of objects. By focusing on the physical structure of the objects of interest, it is possible to remove complex training phases and exploit sensors with lower resolution but wider Field of View (FoV). Our evaluation shows that our approach can achieve comparable (if not better) performance in object labeling and positioning with a significant decrease in processing time than established approaches based on deep learning methods. As a side-effect of using low-density point-clouds, we also better support people privacy as the lower resolution inherently prevents the use of techniques like face recognition.}, keywords = {}, pubstate = {published}, tppubtype = {article} } The advancements in the robotic field have made it possible for service robots to increasingly become part of everyday indoor scenarios. Their ability to operate and reach defined goals depends on the perception and understanding of their surrounding environment. Detecting and positioning objects as well as people in an accurate semantic map are, therefore, essential tasks that a robot needs to carry out. In this work, we walk an alternative path to build semantic maps of indoor scenarios. Instead of relying on high-density sensory input, like the one provided by an RGB-D camera, and resource-intensive processing algorithms, like the ones based on deep learning, we investigate the use of low-density point-clouds provided by 3D LiDARs together with a set of practical segmentation methods for the detection of objects. By focusing on the physical structure of the objects of interest, it is possible to remove complex training phases and exploit sensors with lower resolution but wider Field of View (FoV). Our evaluation shows that our approach can achieve comparable (if not better) performance in object labeling and positioning with a significant decrease in processing time than established approaches based on deep learning methods. As a side-effect of using low-density point-clouds, we also better support people privacy as the lower resolution inherently prevents the use of techniques like face recognition. |
Carlos Medina-Sánchez, Jesús Capitán, Matteo Zella, Pedro José Marrón: Point-Cloud Fast Filter for People Detection with Indoor Service Robots. 2020 Fourth IEEE International Conference on Robotic Computing (IRC), pp. 161-165, 2020. (Type: Inproceedings | Links) @inproceedings{9287928, title = {Point-Cloud Fast Filter for People Detection with Indoor Service Robots}, author = {Carlos Medina-Sánchez and Jesús Capitán and Matteo Zella and Pedro José Marrón}, doi = {10.1109/IRC.2020.00032}, year = {2020}, date = {2020-01-01}, booktitle = {2020 Fourth IEEE International Conference on Robotic Computing (IRC)}, pages = {161-165}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Simon Janzon, Carlos Medina-Sánchez, Matteo Zella, Pedro José Marrón: Person Re-Identification in Human Following Scenarios: An Experience with RGB-D Cameras. 2020 Fourth IEEE International Conference on Robotic Computing (IRC), pp. 424-425, 2020. (Type: Inproceedings | Links) @inproceedings{9287913, title = {Person Re-Identification in Human Following Scenarios: An Experience with RGB-D Cameras}, author = {Simon Janzon and Carlos Medina-Sánchez and Matteo Zella and Pedro José Marrón}, doi = {10.1109/IRC.2020.00076}, year = {2020}, date = {2020-01-01}, booktitle = {2020 Fourth IEEE International Conference on Robotic Computing (IRC)}, pages = {424-425}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
2019 |
Carlos Medina-Sánchez, Matteo Zella, Jesús Capitán, Pedro José Marrón: Efficient Traversability Mapping for Service Robots Using a Point-cloud Fast Filter. Proceedings of the 19th International Conference on Advanced Robotics (ICAR'19), Belo Horizonte, Brazil, 2019. (Type: Inproceedings | ) @inproceedings{medina19:pff, title = {Efficient Traversability Mapping for Service Robots Using a Point-cloud Fast Filter}, author = {Carlos Medina-Sánchez and Matteo Zella and Jesús Capitán and Pedro José Marrón}, year = {2019}, date = {2019-12-02}, booktitle = {Proceedings of the 19th International Conference on Advanced Robotics (ICAR'19)}, address = {Belo Horizonte, Brazil}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |