dc.identifier.uri |
http://dx.doi.org/10.15488/10825 |
|
dc.identifier.uri |
https://www.repo.uni-hannover.de/handle/123456789/10903 |
|
dc.contributor.author |
Schachtschneider, Julia
|
|
dc.contributor.author |
Brenner, Claus
|
|
dc.contributor.editor |
Paparoditis, N.
|
|
dc.contributor.editor |
Mallet, C.
|
|
dc.contributor.editor |
Lafarge, F.
|
|
dc.contributor.editor |
Remondino, Fabio
|
|
dc.contributor.editor |
Toschi, Isabella
|
|
dc.contributor.editor |
Fuse, Takashi
|
|
dc.date.accessioned |
2021-04-27T08:35:57Z |
|
dc.date.available |
2021-04-27T08:35:57Z |
|
dc.date.issued |
2020 |
|
dc.identifier.citation |
Schachtschneider, J.; Brenner, C.: Creating multi-temporal maps of urban environments of improved localization of autonomous vehicles. In: Paparoditis, N. et al. (Eds.): XXIV ISPRS Congress, Commission III : edition 2020. Katlenburg-Lindau : Copernicus Publications, 2020. (ISPRS Archives ; 43,B2), S. 317-323. DOI: https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-317-2020 |
|
dc.description.abstract |
The development of automated and autonomous vehicles requires highly accurate long-term maps of the environment. Urban areas contain a large number of dynamic objects which change over time. Since a permanent observation of the environment is impossible and there will always be a first time visit of an unknown or changed area, a map of an urban environment needs to model such dynamics. In this work, we use LiDAR point clouds from a large long term measurement campaign to investigate temporal changes. The data set was recorded along a 20 km route in Hannover, Germany with a Mobile Mapping System over a period of one year in bi-weekly measurements. The data set covers a variety of different urban objects and areas, weather conditions and seasons. Based on this data set, we show how scene and seasonal effects influence the measurement likelihood, and that multi-temporal maps lead to the best positioning results. © 2020 International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives. |
eng |
dc.language.iso |
eng |
|
dc.publisher |
Katlenburg-Lindau : Copernicus Publications |
|
dc.relation.ispartof |
XXIV ISPRS Congress, Commission II : edition 2020 |
|
dc.relation.ispartofseries |
ISPRS Archives ; 43,B2 |
|
dc.rights |
CC BY 4.0 Unported |
|
dc.rights.uri |
https://creativecommons.org/licenses/by/4.0/ |
|
dc.subject |
3D modelling |
eng |
dc.subject |
dynamic environments |
eng |
dc.subject |
LiDAR |
eng |
dc.subject |
localization |
eng |
dc.subject |
mobile mapping |
eng |
dc.subject.classification |
Konferenzschrift |
ger |
dc.subject.ddc |
550 | Geowissenschaften
|
ger |
dc.title |
Creating multi-temporal maps of urban environments of improved localization of autonomous vehicles |
|
dc.type |
BookPart |
|
dc.type |
Text |
|
dc.relation.essn |
2194-9034 |
|
dc.relation.issn |
1682-1750 |
|
dc.relation.doi |
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-317-2020 |
|
dc.bibliographicCitation.issue |
B2 |
|
dc.bibliographicCitation.volume |
43 |
|
dc.bibliographicCitation.firstPage |
317 |
|
dc.bibliographicCitation.lastPage |
323 |
|
dc.description.version |
publishedVersion |
|
tib.accessRights |
frei zug�nglich |
|