Incremental map refinement of building information using lidar point clouds

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Original languageEnglish
Title of host publicationInternational Archives of the Photogrammetry, Remote Sensing und Spatial Information Sciences
Subtitle of host publicationXXIV ISPRS Congress (2021 edition)
PublisherCopernicus Publications
Pages277-282
Number of pages6
Publication statusPublished - 28 Jun 2021

Publication series

NameInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
PublisherInternational Society for Photogrammetry and Remote Sensing
ISSN (Print)1682-1750

Abstract

For autonomous systems, an accurate and precise map of the environment is of importance. Mapping from LiDAR point clouds is one of the promising ways to generate 3D environment models. However, there are many problems caused by inaccurate data, missing areas, low density of points and sensor noise. Also, it is often not possible or accurate enough to generate a map from only one measurement campaign. In this paper, we propose a method to incrementally refine the map by several measurements from different campaigns and represent the map in a hierarchical way with a measure indicating uncertainty and the level of detail for objects. The idea is thus to store all captured information with a tentative semantics and uncertainty – even when it is not yet complete. Hence, occulated areas are presented as well, which can be possibly improved by the supplemental observation from the next measurement campaign. The proposed 3D environment model framework and the incremental update method are evaluated using LiDAR scans obtained from Riegl Mobile Mapping System.

Keywords

    3D Quality Map, Building Modelling, Incremental Refinement, LiDAR, Mobile Mapping, Point Cloud

ASJC Scopus subject areas

Cite this

Incremental map refinement of building information using lidar point clouds. / Zou, Qianqian; Sester, Monika.
International Archives of the Photogrammetry, Remote Sensing und Spatial Information Sciences: XXIV ISPRS Congress (2021 edition). Copernicus Publications, 2021. p. 277-282 (International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives).

Research output: Chapter in book/report/conference proceedingConference contributionResearchpeer review

Zou, Q & Sester, M 2021, Incremental map refinement of building information using lidar point clouds. in International Archives of the Photogrammetry, Remote Sensing und Spatial Information Sciences: XXIV ISPRS Congress (2021 edition). International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, Copernicus Publications, pp. 277-282. https://doi.org/10.5194/isprs-archives-xliii-b2-2021-277-2021
Zou, Q., & Sester, M. (2021). Incremental map refinement of building information using lidar point clouds. In International Archives of the Photogrammetry, Remote Sensing und Spatial Information Sciences: XXIV ISPRS Congress (2021 edition) (pp. 277-282). (International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives). Copernicus Publications. https://doi.org/10.5194/isprs-archives-xliii-b2-2021-277-2021
Zou Q, Sester M. Incremental map refinement of building information using lidar point clouds. In International Archives of the Photogrammetry, Remote Sensing und Spatial Information Sciences: XXIV ISPRS Congress (2021 edition). Copernicus Publications. 2021. p. 277-282. (International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives). doi: 10.5194/isprs-archives-xliii-b2-2021-277-2021
Zou, Qianqian ; Sester, Monika. / Incremental map refinement of building information using lidar point clouds. International Archives of the Photogrammetry, Remote Sensing und Spatial Information Sciences: XXIV ISPRS Congress (2021 edition). Copernicus Publications, 2021. pp. 277-282 (International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives).
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abstract = "For autonomous systems, an accurate and precise map of the environment is of importance. Mapping from LiDAR point clouds is one of the promising ways to generate 3D environment models. However, there are many problems caused by inaccurate data, missing areas, low density of points and sensor noise. Also, it is often not possible or accurate enough to generate a map from only one measurement campaign. In this paper, we propose a method to incrementally refine the map by several measurements from different campaigns and represent the map in a hierarchical way with a measure indicating uncertainty and the level of detail for objects. The idea is thus to store all captured information with a tentative semantics and uncertainty – even when it is not yet complete. Hence, occulated areas are presented as well, which can be possibly improved by the supplemental observation from the next measurement campaign. The proposed 3D environment model framework and the incremental update method are evaluated using LiDAR scans obtained from Riegl Mobile Mapping System.",
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