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Improved mapping and image segmentation by using semantic information to link aerial images and ground-level information
Örebro University, Department of Technology. (AASS)
Department of Computing and Informatics, University of Lincoln, Lincoln, United Kingdom. (Department of Computing and Informatics)
Örebro University, Department of Technology. (AASS)ORCID iD: 0000-0003-0217-9326
2007 (English)In: Proceedings of the IEEE international conference on advanced robotics: ICAR 2007, 2007, p. 924-929Conference paper, Published paper (Refereed)
Abstract [en]

This paper investigates the use of semantic information to link ground-level occupancy maps and aerial images. In the suggested approach a ground-level semantic map is obtained by a mobile robot equipped with an omnidirectional camera, differential GPS and a laser range finder. The mobile robot uses a virtual sensor for building detection (based on omnidirectional images) to compute the ground-level semantic map, which indicates the probability of the cells being occupied by the wall of a building. These wall estimates from a ground perspective are then matched with edges detected in an aerial image. The result is used to direct a region- and boundary-based segmentation algorithm for building detection in the aerial image. This approach addresses two difficulties simultaneously: 1) the range limitation of mobile robot sensors and 2) the difficulty of detecting buildings in monocular aerial images. With the suggested method building outlines can be detected faster than the mobile robot can explore the area by itself, giving the robot an ability to "see" around corners. At the same time, the approach can compensate for the absence of elevation data in segmentation of aerial images. Our experiments demonstrate that ground-level semantic information (wall estimates) allows to focus the segmentation of the aerial image to find buildings and produce a groundlevel semantic map that covers a larger area than can be built using the onboard sensors along the robot trajectory.

Place, publisher, year, edition, pages
2007. p. 924-929
National Category
Engineering and Technology Computer and Information Sciences
Research subject
Computer and Systems Science
Identifiers
URN: urn:nbn:se:oru:diva-4267OAI: oai:DiVA.org:oru-4267DiVA, id: diva2:138566
Conference
13th IEEE International Conference on Advanced Robotics, ICAR 2007, Jeju Isl, South Korea, Aug. 22-25, 2007
Available from: 2007-12-13 Created: 2007-12-13 Last updated: 2018-06-12Bibliographically approved

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Improved Mapping and Image Segmentation by Using Semantic Information to Link Aerial Images and Ground-Level Information(377 kB)45 downloads
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File name FULLTEXT01.pdfFile size 377 kBChecksum SHA-512
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Persson, MartinLilienthal, Achim J.

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CiteExportLink to record
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Citation style
  • apa
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Language
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  • Other locale
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Output format
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