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Quality-based registration refinement of airborne LiDAR and photogrammetric point clouds
ISPRS Journal of Photogrammetry and Remote Sensing ( IF 12.7 ) Pub Date : 2021-01-04 , DOI: 10.1016/j.isprsjprs.2020.12.005
I. Toschi , E.M. Farella , M. Welponer , F. Remondino

A big challenge in geodata processing is the seamless and accurate integration of airborne LiDAR (Light Detection And Ranging) and photogrammetric point clouds performed by properly considering their high variations in resolution and precision. In this paper we propose a new approach to co-register airborne point clouds acquired by LiDAR sensors and photogrammetric algorithms, assuming that only dense point clouds from both mapping methods are available, without LiDAR raw data nor flight trajectories. First, semantically segmented point clouds are quality-wise evaluated by assigning sensor-specific quality features to each 3D point. Then, these quality features are aggregated in order to assign a score to each 3D point based on its quality. Finally, using a voxel-based structure, a filtering step is performed to select only the best points used for the registration refinement. We assess the performance of the proposed method on two different case studies to demonstrate its advantages compared to a traditional ICP-based approach. The code of the implemented method is available at https://github.com/3DOM-FBK/HyRe.



中文翻译:

机载LiDAR和摄影测量点云的基于质量的配准改进

地理数据处理中的一大挑战是机载LiDAR(光检测和测距)和摄影测量点云的无缝和准确集成,方法是正确考虑其分辨率和精度的高变化。在本文中,我们提出了一种新的方法来共同注册由LiDAR传感器和摄影测量算法获取的机载点云,假设只有两种映射方法中的密集点云都可用,而没有LiDAR原始数据或飞行轨迹。首先,通过将传感器特定的质量特征分配给每个3D点,对按质量进行语义分割的点云进行评估。然后,将这些质量特征进行汇总,以便根据其质量为每个3D点分配得分。最后,使用基于体素的结构,执行过滤步骤以仅选择用于配准细化的最佳点。我们在两个不同的案例研究中评估了所提出方法的性能,以证明其与基于ICP的传统方法相比的优势。可从https://github.com/3DOM-FBK/HyRe获得实现的方法的代码。

更新日期:2021-01-05
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