Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 317-323 |
| Seitenumfang | 7 |
| Fachzeitschrift | International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives |
| Jahrgang | 43 |
| Ausgabenummer | B2 |
| Publikationsstatus | Veröffentlicht - 12 Aug. 2020 |
| Veranstaltung | 2020 24th ISPRS Congress - Technical Commission II - Nice, Virtual, Frankreich Dauer: 31 Aug. 2020 → 2 Sept. 2020 |
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.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Information systems
- Sozialwissenschaften (insg.)
- Geografie, Planung und Entwicklung
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in: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, Jahrgang 43, Nr. B2, 12.08.2020, S. 317-323.
Publikation: Beitrag in Fachzeitschrift › Konferenzaufsatz in Fachzeitschrift › Forschung › Peer-Review
}
TY - JOUR
T1 - CREATING MULTI-TEMPORAL MAPS of URBAN ENVIRONMENTS for IMPROVED LOCALIZATION of AUTONOMOUS VEHICLES
AU - Schachtschneider, Julia
AU - Brenner, Claus
N1 - Funding information: This project is supported by the German Research Foundation (DFG), as part of the Research Training Group i.c.sens, GRK 2159, Integrity and Collaboration in Dynamic Sensor Networks.
PY - 2020/8/12
Y1 - 2020/8/12
N2 - 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.
AB - 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.
KW - 3D Modelling
KW - Dynamic Environments
KW - LiDAR
KW - Localization
KW - Mobile Mapping
UR - http://www.scopus.com/inward/record.url?scp=85091109432&partnerID=8YFLogxK
U2 - 10.5194/isprs-archives-XLIII-B2-2020-317-2020
DO - 10.5194/isprs-archives-XLIII-B2-2020-317-2020
M3 - Conference article
AN - SCOPUS:85091109432
VL - 43
SP - 317
EP - 323
JO - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
JF - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
SN - 1682-1750
IS - B2
T2 - 2020 24th ISPRS Congress - Technical Commission II
Y2 - 31 August 2020 through 2 September 2020
ER -