Details
| Original language | English |
|---|---|
| Pages (from-to) | 335-341 |
| Number of pages | 7 |
| Journal | International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives |
| Volume | 43 |
| Issue number | B2-2022 |
| Publication status | Published - 30 May 2022 |
| Event | 2022 24th ISPRS Congress on Imaging Today, Foreseeing Tomorrow, Commission II - Nice, France Duration: 6 Jun 2022 → 11 Jun 2022 |
Abstract
The quality of environmental perception is of great interest for localization tasks in autonomous systems. Maps, generated from the sensed information, are often used as additional spatial references in these applications. The quantification of the map uncertainties gives an insight into how reliable and complete the map is, avoiding the potential systematic deviation in pose estimation. Mapping 3D buildings in urban areas using Light detection and ranging (LiDAR) point clouds is a challenging task as it is often subject to uncertain error sources in the real world such as sensor noise and occlusions, which should be well represented in the 3D models for the downstream localization tasks. In this paper, we propose a method to model 3D building façades in complex urban scenes with uncertainty quantification, where the uncertainties of windows and façades are indicated in a probabilistic fashion. The potential locations of the missing objects (here: windows) are inferred by the available data and layout patterns with the Monte Carlo (MC) sampling approach. The proposed 3D building model and uncertainty measures are evaluated using the real-world LiDAR point clouds collected by Riegl Mobile Mapping System. The experimental results show that our uncertainty representation conveys the quality information of the estimated locations and shapes for the modelled map objects.
Keywords
- 3D Map, Integrity, LiDAR, Mobile Mapping, Point Cloud, Uncertainty
ASJC Scopus subject areas
- Computer Science(all)
- Information Systems
- Social Sciences(all)
- Geography, Planning and Development
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In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, Vol. 43, No. B2-2022, 30.05.2022, p. 335-341.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - Uncertainty representation and quantification of 3D Models
AU - Zou, Q.
AU - Sester, M.
N1 - Funding Information: This work was funded by the German Research Foundation (DFG) as a part of the Research Training Group GRK2159
PY - 2022/5/30
Y1 - 2022/5/30
N2 - The quality of environmental perception is of great interest for localization tasks in autonomous systems. Maps, generated from the sensed information, are often used as additional spatial references in these applications. The quantification of the map uncertainties gives an insight into how reliable and complete the map is, avoiding the potential systematic deviation in pose estimation. Mapping 3D buildings in urban areas using Light detection and ranging (LiDAR) point clouds is a challenging task as it is often subject to uncertain error sources in the real world such as sensor noise and occlusions, which should be well represented in the 3D models for the downstream localization tasks. In this paper, we propose a method to model 3D building façades in complex urban scenes with uncertainty quantification, where the uncertainties of windows and façades are indicated in a probabilistic fashion. The potential locations of the missing objects (here: windows) are inferred by the available data and layout patterns with the Monte Carlo (MC) sampling approach. The proposed 3D building model and uncertainty measures are evaluated using the real-world LiDAR point clouds collected by Riegl Mobile Mapping System. The experimental results show that our uncertainty representation conveys the quality information of the estimated locations and shapes for the modelled map objects.
AB - The quality of environmental perception is of great interest for localization tasks in autonomous systems. Maps, generated from the sensed information, are often used as additional spatial references in these applications. The quantification of the map uncertainties gives an insight into how reliable and complete the map is, avoiding the potential systematic deviation in pose estimation. Mapping 3D buildings in urban areas using Light detection and ranging (LiDAR) point clouds is a challenging task as it is often subject to uncertain error sources in the real world such as sensor noise and occlusions, which should be well represented in the 3D models for the downstream localization tasks. In this paper, we propose a method to model 3D building façades in complex urban scenes with uncertainty quantification, where the uncertainties of windows and façades are indicated in a probabilistic fashion. The potential locations of the missing objects (here: windows) are inferred by the available data and layout patterns with the Monte Carlo (MC) sampling approach. The proposed 3D building model and uncertainty measures are evaluated using the real-world LiDAR point clouds collected by Riegl Mobile Mapping System. The experimental results show that our uncertainty representation conveys the quality information of the estimated locations and shapes for the modelled map objects.
KW - 3D Map
KW - Integrity
KW - LiDAR
KW - Mobile Mapping
KW - Point Cloud
KW - Uncertainty
UR - http://www.scopus.com/inward/record.url?scp=85132036681&partnerID=8YFLogxK
U2 - 10.5194/isprs-archives-XLIII-B2-2022-335-2022
DO - 10.5194/isprs-archives-XLIII-B2-2022-335-2022
M3 - Conference article
AN - SCOPUS:85132036681
VL - 43
SP - 335
EP - 341
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 - B2-2022
T2 - 2022 24th ISPRS Congress on Imaging Today, Foreseeing Tomorrow, Commission II
Y2 - 6 June 2022 through 11 June 2022
ER -