Benchmarking ULS-TLS Point Cloud Registration Algorithms in Forest Environments
Wangjun Liu, Sheng Nie, Shaobo Xia, Cheng Wang, Jinliang Wang, Xiaohuan Xi, Feng Cheng · IEEE Transactions on Geoscience and Remote Sensing · 2025
Integrating Unmanned aerial vehicle Laser Scanning (ULS) and Terrestrial Laser Scanning (TLS) data in complex forest environments remains a significant challenge. Despite the availability of numerous registration algorithms, robust comparative studies are limited by the lack of reliable multi-platform benchmark datasets. In this study, we introduce the first multiplatform benchmark dataset for ULS-TLS point cloud registration in forests, encompassing 17 plots from seven diverse regions with about 1.56 billion points. The dataset is categorized into three difficulty levels based on overlap ratio and rigid overlap.We evaluated the performance of five registration algorithms against this benchmark. Chen2022 achieved the highest accuracy with a 100% success rate across all difficulty levels. While Wu2024 demonstrated robust performance in lower difficulties but faced challenges in more complex scenarios. We also found that terrain variations and rigid overlap significantly impacted registration accuracy, particularly for algorithms reliant on individual tree positions such as Hyypp¨a2021 and Feng2024. These findings underscore the need for improved data collection strategy, ground filtering techniques, and feature matching algorithms to enhance performance in challenging environments. We present the first openly accessible multi-platform benchmark dataset for forested regions and anticipate that future research will expand this work to additional areas. The dataset can be downloaded from: DatasetDownloadLink.