Scopus
🔓 Açık Erişim
Spatio-Temporal Detection and Filtering of Dynamic Objects in Mobile LiDAR Point Clouds
International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences ISPRS Archives · Ocak 2026
Makale Bilgileri
DergiInternational Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences ISPRS Archives
Yayın TarihiOcak 2026
Cilt / Sayfa48 · 371-378
Scopus ID2-s2.0-105030106875
Erişim🔓 Açık Erişim
Özet
Mobile LiDAR systems are increasingly utilized for high-precision mapping in dynamic environments, yet the presence of moving objects introduces significant noise and distortions in the resulting point clouds. Addressing this challenge, this study proposes a novel and efficient method for detecting and removing moving objects from mobile LiDAR point clouds. The approach involves an initial separation of ground and non-ground points using the Cloth Simulation Filtering (CSF) algorithm, followed by density-based clustering (DBSCAN) of non-ground points. By analyzing the temporal distribution of LiDAR points (gpstime) within each cluster relative to ground points, clusters are classified as either static or dynamic. Dynamic clusters, corresponding to moving objects, are then excluded from the dataset, yielding a refined point cloud that better represents the static environment. The method is implemented in R using various open-source libraries and validated on high-traffic urban datasets acquired with the Riegl VMX-450 mobile LiDAR system. Experimental results demonstrate that the proposed pipeline effectively detects and removes dynamic objects, thereby improving the accuracy and reliability of LiDAR-based mapping in complex, real-world scenarios.
Yazarlar (1)
1
Mustafa Zeybek
ORCID: 0000-0001-8640-1443
Anahtar Kelimeler
Clustering Algorithms
Dynamic Object Removal
Ground Classification
Mobile LiDAR
Point Cloud Processing
Timestamp Analysis
Kurumlar
Selçuk Üniversitesi
Selçuklu Turkey