Vehicle localization by lidar point correlation improved by change detection

Show simple item record

dc.identifier.uri http://dx.doi.org/10.15488/691
dc.identifier.uri http://www.repo.uni-hannover.de/handle/123456789/715
dc.contributor.author Schlichting, Alexander
dc.contributor.author Brenner, Claus
dc.date.accessioned 2016-11-21T07:54:34Z
dc.date.available 2016-11-21T07:54:34Z
dc.date.issued 2016
dc.identifier.citation Schlichting, A.; Brenner, C.: Vehicle localization by lidar point correlation improved by change detection. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives 41 (2016), S. 703-710. DOI: http://dx.doi.org/10.5194/isprsarchives-XLI-B1-703-2016
dc.description.abstract LiDAR sensors are proven sensors for accurate vehicle localization. Instead of detecting and matching features in the LiDAR data, we want to use the entire information provided by the scanners. As dynamic objects, like cars, pedestrians or even construction sites could lead to wrong localization results, we use a change detection algorithm to detect these objects in the reference data. If an object occurs in a certain number of measurements at the same position, we mark it and every containing point as static. In the next step, we merge the data of the single measurement epochs to one reference dataset, whereby we only use static points. Further, we also use a classification algorithm to detect trees. For the online localization of the vehicle, we use simulated data of a vertical aligned automotive LiDAR sensor. As we only want to use static objects in this case as well, we use a random forest classifier to detect dynamic scan points online. Since the automotive data is derived from the LiDAR Mobile Mapping System, we are able to use the labelled objects from the reference data generation step to create the training data and further to detect dynamic objects online. The localization then can be done by a point to image correlation method using only static objects. We achieved a localization standard deviation of about 5 cm (position) and 0.06° (heading), and were able to successfully localize the vehicle in about 93 % of the cases along a trajectory of 13 km in Hannover, Germany. 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 Unported
dc.rights.uri http://creativecommons.org/licenses/by/3.0/
dc.subject Change detection eng
dc.subject Classification eng
dc.subject Correlation eng
dc.subject LiDAR eng
dc.subject Localization eng
dc.subject Mobile mapping eng
dc.subject Chemical detection eng
dc.subject Classification (of information) eng
dc.subject Correlation methods eng
dc.subject Decision trees eng
dc.subject Image matching eng
dc.subject Mapping eng
dc.subject Optical radar eng
dc.subject Remote sensing eng
dc.subject Signal detection eng
dc.subject Vehicles eng
dc.subject Change detection eng
dc.subject Change detection algorithms eng
dc.subject Classification algorithm eng
dc.subject.ddc 500 | Naturwissenschaften ger
dc.subject.ddc 520 | Astronomie, Kartographie ger
dc.title Vehicle localization by lidar point correlation improved by change detection eng
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-B1-703-2016
dc.bibliographicCitation.volume 41
dc.bibliographicCitation.firstPage 703
dc.bibliographicCitation.lastPage 710
dc.description.version publishedVersion
tib.accessRights frei zug�nglich


Files in this item

The following license files are associated with this item:

This item appears in the following Collection(s):

Show simple item record

 

Search the repository


Browse

My Account

Usage Statistics