Multi-LiDAR Mapping for Scene Segmentation in Indoor Environments for Mobile Robots

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Show simple item record González Prieto, Pavel Enrique Mora Velasco, Alicia Garrido Bullón, Luis Santiago Barber Castaño, Ramón Ignacio Moreno Lorente, Luis Enrique 2022-06-06T09:32:26Z 2022-06-06T09:32:26Z 2022-05
dc.identifier.bibliographicCitation Gonzalez, P., Mora, A., Garrido, S., Barber, R., & Moreno, L. (2022). Multi-LiDAR Mapping for Scene Segmentation in Indoor Environments for Mobile Robots. In Sensors, 22, (10), 3690-3710
dc.identifier.issn 1424-3210
dc.description.abstract Nowadays, most mobile robot applications use two-dimensional LiDAR for indoor mapping, navigation, and low-level scene segmentation. However, single data type maps are not enough in a six degree of freedom world. Multi-LiDAR sensor fusion increments the capability of robots to map on different levels the surrounding environment. It exploits the benefits of several data types, counteracting the cons of each of the sensors. This research introduces several techniques to achieve mapping and navigation through indoor environments. First, a scan matching algorithm based on ICP with distance threshold association counter is used as a multi-objective-like fitness function. Then, with Harmony Search, results are optimized without any previous initial guess or odometry. A global map is then built during SLAM, reducing the accumulated error and demonstrating better results than solo odometry LiDAR matching. As a novelty, both algorithms are implemented in 2D and 3D mapping, overlapping the resulting maps to fuse geometrical information at different heights. Finally, a room segmentation procedure is proposed by analyzing this information, avoiding occlusions that appear in 2D maps, and proving the benefits by implementing a door recognition system. Experiments are conducted in both simulated and real scenarios, proving the performance of the proposed algorithms.
dc.description.sponsorship This work was supported by the funding from HEROITEA: Heterogeneous Intelligent Multi-Robot Team for Assistance of Elderly People (RTI2018-095599-B-C21), funded by Spanish Ministerio de Economia y Competitividad, RoboCity2030-DIH-CM, Madrid Robotics Digital Innovation Hub, S2018/NMT-4331, funded by “Programas de Actividades I+D en la Comunidad de Madrid” and cofunded by Structural Funds of the EU. We acknowledge the R&D&I project PLEC2021-007819 funded by MCIN/AEI/ 10.13039/501100011033 and by the European Union NextGenerationEU/PRTR and the Comunidad de Madrid (Spain) under the multiannual agreement with Universidad Carlos III de Madrid (“Excelencia para el Profesorado Universitario’—EPUC3M18) part of the fifth regional research plan 2016–2020.
dc.format.extent 20
dc.language.iso eng
dc.publisher MDPI AG
dc.rights © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
dc.rights Atribución 3.0 España
dc.subject.other Lidar odometry
dc.subject.other Scan matching
dc.subject.other Slam
dc.subject.other Scene segmentation
dc.subject.other Topological
dc.subject.other Harmony search
dc.title Multi-LiDAR Mapping for Scene Segmentation in Indoor Environments for Mobile Robots
dc.type article
dc.subject.eciencia Robótica e Informática Industrial
dc.rights.accessRights openAccess
dc.relation.projectID Comunidad de Madrid. S2018/NMT-4331
dc.relation.projectID Gobierno de España. RTI2018-095599-B-C21
dc.relation.projectID Gobierno de España. PLEC2021-007819
dc.relation.projectID Comunidad de Madrid. EPUC3M18
dc.type.version publishedVersion
dc.identifier.publicationfirstpage 3690
dc.identifier.publicationissue 10
dc.identifier.publicationlastpage 3710
dc.identifier.publicationtitle Sensors
dc.identifier.publicationvolume 22
dc.identifier.uxxi AR/0000030654
dc.contributor.funder Comunidad de Madrid
dc.contributor.funder Ministerio de Economía y Competitividad (España)
dc.contributor.funder Ministerio de Ciencia e Innovación (España)
dc.contributor.funder Universidad Carlos III de Madrid
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