Details
| Original language | English |
|---|---|
| Pages (from-to) | 6579-6586 |
| Number of pages | 8 |
| Journal | IEEE Robotics and Automation Letters |
| Volume | 8 |
| Issue number | 10 |
| Early online date | 9 Aug 2023 |
| Publication status | Published - 5 Sept 2023 |
Abstract
Mapping with uncertainty representation is required in many research domains, especially for localization. Although there are many investigations regarding the uncertainty of the pose estimation of an ego-robot with map information, the quality of the reference maps is often neglected. To avoid potential problems caused by the errors of maps and a lack of uncertainty quantification, an adequate uncertainty measure for the maps is required. In this paper, uncertain building models with abstract map surfaces using Gaussian Processes (GPs) are proposed to describe the map uncertainty in a probabilistic way. To reduce the redundant computation for simple planar objects, extracted facets from a Gaussian Mixture Model (GMM) are combined with an implicit GP map, also employing local GP-block techniques. The proposed method is evaluated on LiDAR point clouds of city buildings collected by a mobile mapping system. Compared to the performance of other methods such as Octomap, GP Occupancy Map (GPOM), Bayesian Generalized Kernel Inference (BGKOctomap), Local automatic relevance determination Hilbert map (LARD-HM) and Gaussian Implicit Surface map (GPIS), our method achieves a higher Precision-Recall AUC for the evaluated buildings.
Keywords
- Buildings, Kernel, Laser-based, Location awareness, Mapping, Measurement uncertainty, Point cloud compression, Probabilistic logic, Probability and statistical methods, Uncertainty, Uncertainty representation, uncertainty representation, probability and statistical methods, laser-based
ASJC Scopus subject areas
- Engineering(all)
- Mechanical Engineering
- Mathematics(all)
- Control and Optimization
- Computer Science(all)
- Artificial Intelligence
- Computer Science(all)
- Human-Computer Interaction
- Engineering(all)
- Control and Systems Engineering
- Computer Science(all)
- Computer Vision and Pattern Recognition
- Engineering(all)
- Biomedical Engineering
- Computer Science(all)
- Computer Science Applications
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In: IEEE Robotics and Automation Letters, Vol. 8, No. 10, 05.09.2023, p. 6579-6586.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Gaussian Process Mapping of Uncertain Building Models with GMM as Prior
AU - Zou, Qianqian
AU - Sester, Monika
AU - Brenner, Claus
PY - 2023/9/5
Y1 - 2023/9/5
N2 - Mapping with uncertainty representation is required in many research domains, especially for localization. Although there are many investigations regarding the uncertainty of the pose estimation of an ego-robot with map information, the quality of the reference maps is often neglected. To avoid potential problems caused by the errors of maps and a lack of uncertainty quantification, an adequate uncertainty measure for the maps is required. In this paper, uncertain building models with abstract map surfaces using Gaussian Processes (GPs) are proposed to describe the map uncertainty in a probabilistic way. To reduce the redundant computation for simple planar objects, extracted facets from a Gaussian Mixture Model (GMM) are combined with an implicit GP map, also employing local GP-block techniques. The proposed method is evaluated on LiDAR point clouds of city buildings collected by a mobile mapping system. Compared to the performance of other methods such as Octomap, GP Occupancy Map (GPOM), Bayesian Generalized Kernel Inference (BGKOctomap), Local automatic relevance determination Hilbert map (LARD-HM) and Gaussian Implicit Surface map (GPIS), our method achieves a higher Precision-Recall AUC for the evaluated buildings.
AB - Mapping with uncertainty representation is required in many research domains, especially for localization. Although there are many investigations regarding the uncertainty of the pose estimation of an ego-robot with map information, the quality of the reference maps is often neglected. To avoid potential problems caused by the errors of maps and a lack of uncertainty quantification, an adequate uncertainty measure for the maps is required. In this paper, uncertain building models with abstract map surfaces using Gaussian Processes (GPs) are proposed to describe the map uncertainty in a probabilistic way. To reduce the redundant computation for simple planar objects, extracted facets from a Gaussian Mixture Model (GMM) are combined with an implicit GP map, also employing local GP-block techniques. The proposed method is evaluated on LiDAR point clouds of city buildings collected by a mobile mapping system. Compared to the performance of other methods such as Octomap, GP Occupancy Map (GPOM), Bayesian Generalized Kernel Inference (BGKOctomap), Local automatic relevance determination Hilbert map (LARD-HM) and Gaussian Implicit Surface map (GPIS), our method achieves a higher Precision-Recall AUC for the evaluated buildings.
KW - Buildings
KW - Kernel
KW - Laser-based
KW - Location awareness
KW - Mapping
KW - Measurement uncertainty
KW - Point cloud compression
KW - Probabilistic logic
KW - Probability and statistical methods
KW - Uncertainty
KW - Uncertainty representation
KW - uncertainty representation
KW - probability and statistical methods
KW - laser-based
UR - http://www.scopus.com/inward/record.url?scp=85167803746&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2212.07271
DO - 10.48550/arXiv.2212.07271
M3 - Article
VL - 8
SP - 6579
EP - 6586
JO - IEEE Robotics and Automation Letters
JF - IEEE Robotics and Automation Letters
SN - 2377-3766
IS - 10
ER -