Skyline matching based camera orientation from images and mobile mapping point clouds
Mobile Mapping is widely used for collecting large amounts of geo-referenced data. An important role plays sensor fusion, in order to evaluate multiple sensors such as laser scanner and cameras jointly. This requires to determine the relative orientation between sensors. Based on data of a <i>...
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doaj-2f228f0615b44a6ca8a00a5483e42fd12020-11-25T00:42:45ZengCopernicus PublicationsISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2194-90422194-90502014-05-01II-518118810.5194/isprsannals-II-5-181-2014Skyline matching based camera orientation from images and mobile mapping point cloudsS. Hofmann0D. Eggert1C. Brenner2Institute of Cartography and Geoinformatics Leibniz Universität Hannover Appelstrasse 9A, 30167 Hannover, GermanyInstitute of Cartography and Geoinformatics Leibniz Universität Hannover Appelstrasse 9A, 30167 Hannover, GermanyInstitute of Cartography and Geoinformatics Leibniz Universität Hannover Appelstrasse 9A, 30167 Hannover, GermanyMobile Mapping is widely used for collecting large amounts of geo-referenced data. An important role plays sensor fusion, in order to evaluate multiple sensors such as laser scanner and cameras jointly. This requires to determine the relative orientation between sensors. Based on data of a <i>RIEGL</i> VMX-250 mobile mapping system equipped with two laser scanners, four optional cameras, and a highly precise GNSS/IMU system, we propose an approach to improve camera orientations. A manually determined orientation is used as an initial approximation for matching a large number of points in optical images and the corresponding projected scan images. The search space of the point correspondences is reduced to skylines found in both the optical as well as the scan image. The skyline determination is based on alpha shapes, the actual matching is done via an adapted ICP algorithm. The approximate values of the relative orientation are used as starting values for an iterative resection process. Outliers are removed at several stages of the process. Our approach is fully automatic and improves the camera orientation significantly.http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/II-5/181/2014/isprsannals-II-5-181-2014.pdf |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
S. Hofmann D. Eggert C. Brenner |
spellingShingle |
S. Hofmann D. Eggert C. Brenner Skyline matching based camera orientation from images and mobile mapping point clouds ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
author_facet |
S. Hofmann D. Eggert C. Brenner |
author_sort |
S. Hofmann |
title |
Skyline matching based camera orientation from images and mobile mapping point clouds |
title_short |
Skyline matching based camera orientation from images and mobile mapping point clouds |
title_full |
Skyline matching based camera orientation from images and mobile mapping point clouds |
title_fullStr |
Skyline matching based camera orientation from images and mobile mapping point clouds |
title_full_unstemmed |
Skyline matching based camera orientation from images and mobile mapping point clouds |
title_sort |
skyline matching based camera orientation from images and mobile mapping point clouds |
publisher |
Copernicus Publications |
series |
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
issn |
2194-9042 2194-9050 |
publishDate |
2014-05-01 |
description |
Mobile Mapping is widely used for collecting large amounts of geo-referenced data. An important role plays sensor fusion, in order to
evaluate multiple sensors such as laser scanner and cameras jointly. This requires to determine the relative orientation between sensors.
Based on data of a <i>RIEGL</i> VMX-250 mobile mapping system equipped with two laser scanners, four optional cameras, and a highly
precise GNSS/IMU system, we propose an approach to improve camera orientations. A manually determined orientation is used as an
initial approximation for matching a large number of points in optical images and the corresponding projected scan images. The search
space of the point correspondences is reduced to skylines found in both the optical as well as the scan image. The skyline determination
is based on alpha shapes, the actual matching is done via an adapted ICP algorithm. The approximate values of the relative orientation
are used as starting values for an iterative resection process. Outliers are removed at several stages of the process. Our approach is fully
automatic and improves the camera orientation significantly. |
url |
http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/II-5/181/2014/isprsannals-II-5-181-2014.pdf |
work_keys_str_mv |
AT shofmann skylinematchingbasedcameraorientationfromimagesandmobilemappingpointclouds AT deggert skylinematchingbasedcameraorientationfromimagesandmobilemappingpointclouds AT cbrenner skylinematchingbasedcameraorientationfromimagesandmobilemappingpointclouds |
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