3D feature point extraction from LiDAR data using a neural network

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dc.identifier.uri http://dx.doi.org/10.15488/690
dc.identifier.uri http://www.repo.uni-hannover.de/handle/123456789/714
dc.contributor.author Feng, Yu
dc.contributor.author Schlichting, Alexander
dc.contributor.author Brenner, Claus
dc.date.accessioned 2016-11-21T07:54:33Z
dc.date.available 2016-11-21T07:54:33Z
dc.date.issued 2016
dc.identifier.citation Feng, Y.; Schlichting, A.; Brenner, C.: 3D feature point extraction from LiDAR data using a neural network. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives 41 (2016), S. 563-569. DOI: http://dx.doi.org/10.5194/isprsarchives-XLI-B1-563-2016
dc.description.abstract Accurate positioning of vehicles plays an important role in autonomous driving. In our previous research on landmark-based positioning, poles were extracted both from reference data and online sensor data, which were then matched to improve the positioning accuracy of the vehicles. However, there are environments which contain only a limited number of poles. 3D feature points are one of the proper alternatives to be used as landmarks. They can be assumed to be present in the environment, independent of certain object classes. To match the LiDAR data online to another LiDAR derived reference dataset, the extraction of 3D feature points is an essential step. In this paper, we address the problem of 3D feature point extraction from LiDAR datasets. Instead of hand-crafting a 3D feature point extractor, we propose to train it using a neural network. In this approach, a set of candidates for the 3D feature points is firstly detected by the Shi-Tomasi corner detector on the range images of the LiDAR point cloud. Using a back propagation algorithm for the training, the artificial neural network is capable of predicting feature points from these corner candidates. The training considers not only the shape of each corner candidate on 2D range images, but also their 3D features such as the curvature value and surface normal value in z axis, which are calculated directly based on the LiDAR point cloud. Subsequently the extracted feature points on the 2D range images are retrieved in the 3D scene. The 3D feature points extracted by this approach are generally distinctive in the 3D space. Our test shows that the proposed method is capable of providing a sufficient number of repeatable 3D feature points for the matching task. The feature points extracted by this approach have great potential to be used as landmarks for a better localization of vehicles. eng
dc.language.iso eng
dc.publisher Göttingen : Copernicus GmbH
dc.relation.ispartof 23rd International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Congress, ISPRS 2016, 12–19 July 2016, Prague, Czech Republic
dc.relation.ispartofseries International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives 41 (2016)
dc.rights CC BY 3.0
dc.rights.uri http://creativecommons.org/licenses/by/3.0/
dc.subject 3D feature points extraction eng
dc.subject LiDAR eng
dc.subject Mobile mapping system eng
dc.subject Neural network eng
dc.subject Backpropagation eng
dc.subject Backpropagation algorithms eng
dc.subject Edge detection eng
dc.subject Extraction eng
dc.subject Image matching eng
dc.subject Neural networks eng
dc.subject Poles eng
dc.subject Remote sensing eng
dc.subject Vehicles eng
dc.subject Autonomous driving eng
dc.subject Corner detector eng
dc.subject Feature point extraction eng
dc.subject Feature points extraction eng
dc.subject Lidar point clouds eng
dc.subject.ddc 500 | Naturwissenschaften ger
dc.subject.ddc 520 | Astronomie, Kartographie ger
dc.title 3D feature point extraction from LiDAR data using a neural network
dc.type article
dc.type conferenceObject
dc.type Text
dc.relation.issn 1682-1750
dc.relation.doi http://dx.doi.org/10.5194/isprsarchives-XLI-B1-563-2016
dc.bibliographicCitation.volume 41
dc.bibliographicCitation.firstPage 563
dc.bibliographicCitation.lastPage 569
dc.description.version publishedVersion
tib.accessRights frei zug�nglich


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