Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | 2025 International Conference on Indoor Positioning and Indoor Navigation (IPIN) |
| Herausgeber/-innen | Jari Nurmi, Simona Lohan, Aleksandr Ometov, Lucie Klus, Christopher Mutschler, Joaquin Torres-Sospedra |
| Seiten | 1-6 |
| Seitenumfang | 6 |
| ISBN (elektronisch) | 979-8-3315-5680-8 |
| Publikationsstatus | Veröffentlicht - 15 Sept. 2025 |
Abstract
Accurate surface normal estimation is critical for LiDAR-based tasks such as mapping, localization, and scene understanding. However, traditional 3D neighborhood selection methods often struggle with sparse point clouds due to uneven density and sensor limitations. In this paper, we propose a geometry-aware approach that leverages the structured 2D range view to select more consistent neighborhoods for normal estimation. By combining adaptive windowing in range space with distance-based filtering, our method provides robust and accurate normal vectors across both dense and sparse regions. We integrate this method into a lightweight SLAM framework and evaluate it using indoor LiDAR data from a Velodyne-16 sensor. The results demonstrate improved normal estimation and enhanced localization and mapping performance, highlighting the method's effectiveness for indoor robotic applications.
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2025 International Conference on Indoor Positioning and Indoor Navigation (IPIN). Hrsg. / Jari Nurmi; Simona Lohan; Aleksandr Ometov; Lucie Klus; Christopher Mutschler; Joaquin Torres-Sospedra. 2025. S. 1-6.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Range-View-Based Normal Estimation for Sparse LiDAR SLAM.
AU - Mortazavi, Faezeh S.
AU - Brenner, Claus
AU - Sester, Monika
N1 - Publisher Copyright: © 2025 IEEE.
PY - 2025/9/15
Y1 - 2025/9/15
N2 - Accurate surface normal estimation is critical for LiDAR-based tasks such as mapping, localization, and scene understanding. However, traditional 3D neighborhood selection methods often struggle with sparse point clouds due to uneven density and sensor limitations. In this paper, we propose a geometry-aware approach that leverages the structured 2D range view to select more consistent neighborhoods for normal estimation. By combining adaptive windowing in range space with distance-based filtering, our method provides robust and accurate normal vectors across both dense and sparse regions. We integrate this method into a lightweight SLAM framework and evaluate it using indoor LiDAR data from a Velodyne-16 sensor. The results demonstrate improved normal estimation and enhanced localization and mapping performance, highlighting the method's effectiveness for indoor robotic applications.
AB - Accurate surface normal estimation is critical for LiDAR-based tasks such as mapping, localization, and scene understanding. However, traditional 3D neighborhood selection methods often struggle with sparse point clouds due to uneven density and sensor limitations. In this paper, we propose a geometry-aware approach that leverages the structured 2D range view to select more consistent neighborhoods for normal estimation. By combining adaptive windowing in range space with distance-based filtering, our method provides robust and accurate normal vectors across both dense and sparse regions. We integrate this method into a lightweight SLAM framework and evaluate it using indoor LiDAR data from a Velodyne-16 sensor. The results demonstrate improved normal estimation and enhanced localization and mapping performance, highlighting the method's effectiveness for indoor robotic applications.
UR - http://www.scopus.com/inward/record.url?scp=105022158714&partnerID=8YFLogxK
U2 - 10.1109/IPIN66788.2025.11213288
DO - 10.1109/IPIN66788.2025.11213288
M3 - Conference contribution
SN - 979-8-3315-5681-5
SP - 1
EP - 6
BT - 2025 International Conference on Indoor Positioning and Indoor Navigation (IPIN)
A2 - Nurmi, Jari
A2 - Lohan, Simona
A2 - Ometov, Aleksandr
A2 - Klus, Lucie
A2 - Mutschler, Christopher
A2 - Torres-Sospedra, Joaquin
ER -