Comparison of 3D scan matching techniques for autonomous robot navigation in urban and agricultural environments

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dc.contributor.authorGuevara, Javier
dc.contributor.authorGene-Mola, Jordi
dc.contributor.authorGregorio, Eduard
dc.contributor.authorTorres-Torriti, Miguel
dc.contributor.authorReina, Giulio
dc.contributor.authorAuat Cheein, Fernando A.
dc.date.accessioned2025-01-20T22:19:34Z
dc.date.available2025-01-20T22:19:34Z
dc.date.issued2021
dc.description.abstractGlobal navigation satellite system (GNSS) is the standard solution for solving the localization problem in outdoor environments, but its signal might be lost when driving in dense urban areas or in the presence of heavy vegetation or overhanging canopies. Hence, there is a need for alternative or complementary localization methods for autonomous driving. In recent years, exteroceptive sensors have gained much attention due to significant improvements in accuracy and cost-effectiveness, especially for 3D range sensors. By registering two successive 3D scans, known as scan matching, it is possible to estimate the pose of a vehicle. This work aims to provide in-depth analysis and comparison of the state-of-the-art 3D scan matching approaches as a solution to the localization problem of autonomous vehicles. Eight techniques (deterministic and probabilistic) are investigated: iterative closest point (with three different embodiments), normal distribution transform, coherent point drift, Gaussian mixture model, support vector-parametrized Gaussian mixture and the particle filter implementation. They are demonstrated in long path trials in both urban and agricultural environments and compared in terms of accuracy and consistency. On the one hand, most of the techniques can be successfully used in urban scenarios with the probabilistic approaches that show the best accuracy. On the other hand, agricultural settings have proved to be more challenging with significant errors even in short distance trials due to the presence of featureless natural objects. The results and discussion of this work will provide a guide for selecting the most suitable method and will encourage building of improvements on the identified limitations. (C) 2021 Society of PhotoOptical Instrumentation Engineers (SPIE)
dc.fuente.origenWOS
dc.identifier.doi10.1117/1.JRS.15.024508
dc.identifier.eissn1931-3195
dc.identifier.urihttps://doi.org/10.1117/1.JRS.15.024508
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/94600
dc.identifier.wosidWOS:000658967700001
dc.issue.numero2
dc.language.isoen
dc.revistaJournal of applied remote sensing
dc.rightsacceso restringido
dc.subjectautonomous vehicles
dc.subject3D point cloud registration
dc.subjectmobile robot sensing
dc.subjectrobot localization
dc.titleComparison of 3D scan matching techniques for autonomous robot navigation in urban and agricultural environments
dc.typeartículo
dc.volumen15
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
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