Assessing temporal behavior in lidar point clouds of urban environments

Show simple item record

dc.identifier.uri http://dx.doi.org/10.15488/1694
dc.identifier.uri http://www.repo.uni-hannover.de/handle/123456789/1719
dc.contributor.author Schachtschneider, Julia
dc.contributor.author Schlichting, Alexander
dc.contributor.author Brenner, Claus
dc.date.accessioned 2017-07-17T07:35:03Z
dc.date.available 2017-07-17T07:35:03Z
dc.date.issued 2017
dc.identifier.citation Schachtschneider, J.; Schlichting, A.; Brenner, C.: Assessing temporal behavior in lidar point clouds of urban environments. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives 42 (2017), Nr. 1W1, S. 543-550. DOI: https://doi.org/10.5194/isprs-archives-XLII-1-W1-543-2017
dc.description.abstract Self-driving cars and robots that run autonomously over long periods of time need high-precision and up-to-date models of the changing environment. The main challenge for creating long term maps of dynamic environments is to identify changes and adapt the map continuously. Changes can occur abruptly, gradually, or even periodically. In this work, we investigate how dense mapping data of several epochs can be used to identify the temporal behavior of the environment. This approach anticipates possible future scenarios where a large fleet of vehicles is equipped with sensors which continuously capture the environment. This data is then being sent to a cloud based infrastructure, which aligns all datasets geometrically and subsequently runs scene analysis on it, among these being the analysis for temporal changes of the environment. Our experiments are based on a LiDAR mobile mapping dataset which consists of 150 scan strips (a total of about 1 billion points), which were obtained in multiple epochs. Parts of the scene are covered by up to 28 scan strips. The time difference between the first and last epoch is about one year. In order to process the data, the scan strips are aligned using an overall bundle adjustment, which estimates the surface (about one billion surface element unknowns) as well as 270,000 unknowns for the adjustment of the exterior orientation parameters. After this, the surface misalignment is usually below one centimeter. In the next step, we perform a segmentation of the point clouds using a region growing algorithm. The segmented objects and the aligned data are then used to compute an occupancy grid which is filled by tracing each individual LiDAR ray from the scan head to every point of a segment. As a result, we can assess the behavior of each segment in the scene and remove voxels from temporal objects from the global occupancy grid. eng
dc.description.sponsorship DFG/GRK/2159
dc.language.iso eng
dc.publisher London : International Society for Photogrammetry and Remote Sensing
dc.relation.ispartof ISPRS Hannover Workshop 2017 on High-Resolution Earth Imaging for Geospatial Information, HRIGI 2017, City Models, Roads and Traffic , CMRT 2017, Image Sequence Analysis, ISA 2017, European Calibration and Orientation Workshop, EuroCOW 2017; June 6-9 2017, Hannover, Germany
dc.relation.ispartofseries International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives 42 (2017), Nr. 1W1
dc.rights CC BY 3.0
dc.rights.uri https://creativecommons.org/licenses/by/3.0/
dc.subject Alignment eng
dc.subject Change detection eng
dc.subject LiDAR eng
dc.subject Mobile mapping eng
dc.subject Strip adjustment eng
dc.subject Alignment eng
dc.subject Fleet operations eng
dc.subject Image segmentation eng
dc.subject Mapping eng
dc.subject Three dimensional computer graphics eng
dc.subject Change detection eng
dc.subject Changing environment eng
dc.subject Dynamic environments eng
dc.subject Exterior orientation parameters eng
dc.subject Lidar point clouds eng
dc.subject Mobile mapping eng
dc.subject Region growing algorithm eng
dc.subject Strip adjustment eng
dc.subject Optical radar eng
dc.subject.ddc 550 | Geowissenschaften ger
dc.title Assessing temporal behavior in lidar point clouds of urban environments
dc.type article
dc.type conferenceObject
dc.type Text
dc.relation.issn 1682-1750
dc.relation.doi https://doi.org/10.5194/isprs-archives-XLII-1-W1-543-2017
dc.bibliographicCitation.issue 1W1
dc.bibliographicCitation.volume 42
dc.bibliographicCitation.firstPage 543
dc.bibliographicCitation.lastPage 550
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