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3D Spectral Graph Wavelet Point Signatures in Pre-Processing Stage for Mobile Laser Scanning Point Cloud Registration in Unstructured Orchard Environments

Identificadors del recurs

https://doi.org/10.1109/JSEN.2021.3129340

1530-437X

http://hdl.handle.net/10459.1/72508

Procedència

(Universitat de Lleida. Repositori Obert)

Fitxa

Títol:: 3D Spectral Graph Wavelet Point Signatures in Pre-Processing Stage for Mobile Laser Scanning Point Cloud Registration in Unstructured Orchard Environments
Matèria:: Spectral analysis; Filtering algorithms; 3D point cloud registration; Robot localization
Descripció:: The use of three-dimensional registration techniques is an important component for sensor-based localization and mapping. Several approaches have been proposed to align three-dimensional data, obtaining meaningful results in structured scenarios. However, the increased use of high-frame-rate 3D sensors has lead to more challenging application scenarios here the performance of registration techniques may degrade significantly. In order to improve the accuracy of the procedure, different works have considered a representative subset of points while preserving application-dependent features for registration. In this work, we tackle such a problem, considering the use of a general feature-extraction operator in the spectral domain as a prior step to the registration. The proposed spectral strategies use three wavelet transforms that are evaluated along with four well-known registration techniques. The methodology was experimentally validated in a dense orchard environment. The results show that the probability of failure in registration can be reduced up to 12.04% for the evaluated approaches, leading to a significant increase in the localization accuracy. Those results validate the effectiveness and efficiency of the spectral-assisted registration algorithms in an agricultural setting and motivate their usage for a wider range of applications.; This project has been supported by the National Agency of Research and Development (ANID, ex-Conicyt) under Fondecyt grant 1171760, Basal grant FB0008 and National Agency for Research and Devel- opment (ANID)/ PCHA/ Doctorado Nacional/ 2020-21200700. The authors would like to thank to Secretaria d’Universitats i Recerca del Departament d’Empresa i Coneixement de la Generalitat de Catalunya (grant 2017 SGR 646), the Spanish Ministry of Science, Innovation and Universities (project RTI2018-094222-B-I00) for partially funding this research. The Spanish Ministry of Education is thanked for Mr. J. Gene ́’s pre-doctoral fellowships (FPU15/03355). We would also like to thank Nufri (especially Santiago Salamero and Oriol Morreres) for their support during data acquisition.
Idioma:: English
Relació:: info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-094222-B-I00/ES/TECNOLOGIAS DE AGRICULTURA DE PRECISION PARA OPTIMIZAR EL MANEJO DEL DOSEL FOLIAR Y LA PROTECCION FITOSANITARIA SOSTENIBLE EN PLANTACIONES FRUTALES/; Versió postprint del document publicat a: https://doi.org/10.1109/JSEN.2021.3129340; IEEE Sensors Journal, 2021
Autor/Productor:: Guevara, Javier; Gené Mola, Jordi; Gregorio López, Eduard; Auat Cheein, Fernando A.
Editor:: Institute of Electrical and Electronics Engineers
Drets:: (c) IEEE, 2021; info:eu-repo/semantics/openAccess
Data:: 2021-12-09T11:38:31Z; 2021-11-18
Tipus de recurs:: info:eu-repo/semantics/article; info:eu-repo/semantics/acceptedVersion
Format:: application/pdf

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2. < dc:creator > Guevara, Javier </ dc:creator >
3. < dc:creator > Gené Mola, Jordi </ dc:creator >
4. < dc:creator > Gregorio López, Eduard </ dc:creator >
5. < dc:creator > Auat Cheein, Fernando A. </ dc:creator >
6. < dc:subject > Spectral analysis </ dc:subject >
7. < dc:subject > Filtering algorithms </ dc:subject >
8. < dc:subject > 3D point cloud registration </ dc:subject >
9. < dc:subject > Robot localization </ dc:subject >
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5. < creator > Auat Cheein, Fernando A. </ creator >
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        < field name =" value " > The use of three-dimensional registration techniques is an important component for sensor-based localization and mapping. Several approaches have been proposed to align three-dimensional data, obtaining meaningful results in structured scenarios. However, the increased use of high-frame-rate 3D sensors has lead to more challenging application scenarios here the performance of registration techniques may degrade significantly. In order to improve the accuracy of the procedure, different works have considered a representative subset of points while preserving application-dependent features for registration. In this work, we tackle such a problem, considering the use of a general feature-extraction operator in the spectral domain as a prior step to the registration. The proposed spectral strategies use three wavelet transforms that are evaluated along with four well-known registration techniques. The methodology was experimentally validated in a dense orchard environment. The results show that the probability of failure in registration can be reduced up to 12.04% for the evaluated approaches, leading to a significant increase in the localization accuracy. Those results validate the effectiveness and efficiency of the spectral-assisted registration algorithms in an agricultural setting and motivate their usage for a wider range of applications. </ field >
        
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        < field name =" value " > This project has been supported by the National Agency of Research and Development (ANID, ex-Conicyt) under Fondecyt grant 1171760, Basal grant FB0008 and National Agency for Research and Devel- opment (ANID)/ PCHA/ Doctorado Nacional/ 2020-21200700. The authors would like to thank to Secretaria d’Universitats i Recerca del Departament d’Empresa i Coneixement de la Generalitat de Catalunya (grant 2017 SGR 646), the Spanish Ministry of Science, Innovation and Universities (project RTI2018-094222-B-I00) for partially funding this research. The Spanish Ministry of Education is thanked for Mr. J. Gene ́’s pre-doctoral fellowships (FPU15/03355). We would also like to thank Nufri (especially Santiago Salamero and Oriol Morreres) for their support during data acquisition. </ field >
        
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      1. < field name =" value " > info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-094222-B-I00/ES/TECNOLOGIAS DE AGRICULTURA DE PRECISION PARA OPTIMIZAR EL MANEJO DEL DOSEL FOLIAR Y LA PROTECCION FITOSANITARIA SOSTENIBLE EN PLANTACIONES FRUTALES/ </ field >
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