dc.creator | Sánchez, Carlos Medina | es |
dc.creator | Zella, Matteo | es |
dc.creator | Capitán Fernández, Jesús | es |
dc.creator | Marrón, P. J. | es |
dc.date.accessioned | 2020-12-02T09:17:43Z | |
dc.date.available | 2020-12-02T09:17:43Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | Sánchez, C.M., Zella, M., Capitán Fernández, Jesús y Marrón, P.J. (2020). Semantic Mapping with Low-Density Point-Clouds for Service Robots in Indoor Environments. Applied Sciences, 10 (20), 1-18. | |
dc.identifier.issn | 2076-3417 | es |
dc.identifier.uri | https://hdl.handle.net/11441/102917 | |
dc.description | número de art. 7154 | es |
dc.description.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. | es |
dc.format | application/pdf | es |
dc.format.extent | 18 | es |
dc.language.iso | eng | es |
dc.publisher | MDPI AG | es |
dc.relation.ispartof | Applied Sciences, 10 (20), 1-18. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Semantic mapping | es |
dc.subject | Pointcloud-based sensors | es |
dc.subject | Service robots | es |
dc.title | Semantic Mapping with Low-Density Point-Clouds for Service Robots in Indoor Environments | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática | es |
dc.relation.publisherversion | https://www.mdpi.com/2076-3417/10/20/7154 | es |
dc.identifier.doi | 10.3390/app10207154 | es |
dc.contributor.group | Universidad de Sevilla. TEP -151 Robótica, Visión y Control | es |
dc.journaltitle | Applied Sciences | es |
dc.publication.volumen | 10 | es |
dc.publication.issue | 20 | es |
dc.publication.initialPage | 1 | es |
dc.publication.endPage | 18 | es |