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Occlusion detection of traffic signs by voxel-based ray tracing using highly detailed models and MLS point clouds of vegetation
International Journal of Applied Earth Observation and Geoinformation ( IF 7.5 ) Pub Date : 2022-09-20 , DOI: 10.1016/j.jag.2022.103017
Philipp-Roman Hirt, Jonathan Holtkamp, Ludwig Hoegner, Yusheng Xu, Uwe Stilla

Visibility analysis plays a vital role in the design and placing of traffic signs in the urban street environment. This work investigates the occlusion detection of traffic lights and traffic signs caused by vegetation. The presented analysis method is built upon the inputs from the expected situation reflected by a highly detailed 3D city model and the as-is situation captured by 3D Mobile Laser Scanning (MLS). The model contains the location and orientation of streets, traffic lights, and traffic signs; the measurements add detail on irregular-shaped and morphing objects such as vegetation, respectively. The analysis covers the visibility of traffic lights and traffic signs by ray-tracing in an occupancy grid that is generated by the voxelization of the space. The voxels facilitate the distinction between occupied and empty space. The identification of unknown volumes is added and considered in the decision process, to cope with the regions invisible to the sensor. As output, we provide a visibility metric and detailed 3D space descriptions on different levels of granularity, including the knowledge of the semantic classes of traversed voxels. During the whole process, the awareness of unknown volumes is added to an otherwise binary decision between visible and invisible targets. Experiments are conducted on the TUM-MLS-2016 dataset. Results demonstrate that the proposed method is feasible for the detection of occlusions by vegetation in the street scenario, and reveal that the identification of unknown volumes proves necessary for a reliable interpretation of the measurements.



中文翻译:

使用高度详细的模型和植被的 MLS 点云通过基于体素的光线追踪对交通标志的遮挡检测

能见度分析在城市街道环境中交通标志的设计和放置中起着至关重要的作用。这项工作研究了由植被引起的交通信号灯和交通标志的遮挡检测。所提出的分析方法基于高度详细的 3D 城市模型所反映的预期情况和 3D 移动激光扫描 (MLS) 捕获的现状的输入。该模型包含街道、红绿灯和交通标志的位置和方向;测量结果分别增加了植被等不规则形状和变形物体的细节。该分析通过空间体素化生成的占用网格中的光线跟踪来涵盖交通信号灯和交通标志的可见性。体素有助于区分占用空间和空白空间。在决策过程中添加和考虑未知体积的识别,以应对传感器不可见的区域。作为输出,我们提供了不同粒度级别的可见性度量和详细的 3D 空间描述,包括遍历体素的语义类别的知识。在整个过程中,未知体积的意识被添加到可见和不可见目标之间的二元决策中。实验在 TUM-MLS-2016 数据集上进行。结果表明,所提出的方法对于在街道场景中检测植被遮挡是可行的,并表明未知体积的识别对于可靠地解释测量值是必要的。以应对传感器不可见的区域。作为输出,我们提供了不同粒度级别的可见性度量和详细的 3D 空间描述,包括遍历体素的语义类别的知识。在整个过程中,未知体积的意识被添加到可见和不可见目标之间的二元决策中。实验在 TUM-MLS-2016 数据集上进行。结果表明,所提出的方法对于在街道场景中检测植被遮挡是可行的,并表明未知体积的识别对于可靠地解释测量值是必要的。以应对传感器不可见的区域。作为输出,我们提供了不同粒度级别的可见性度量和详细的 3D 空间描述,包括遍历体素的语义类别的知识。在整个过程中,未知体积的意识被添加到可见和不可见目标之间的二元决策中。实验在 TUM-MLS-2016 数据集上进行。结果表明,所提出的方法对于在街道场景中检测植被遮挡是可行的,并表明未知体积的识别对于可靠地解释测量值是必要的。包括遍历体素的语义类别的知识。在整个过程中,未知体积的意识被添加到可见和不可见目标之间的二元决策中。实验在 TUM-MLS-2016 数据集上进行。结果表明,所提出的方法对于在街道场景中检测植被遮挡是可行的,并表明未知体积的识别对于可靠地解释测量值是必要的。包括遍历体素的语义类别的知识。在整个过程中,未知体积的意识被添加到可见和不可见目标之间的二元决策中。实验在 TUM-MLS-2016 数据集上进行。结果表明,所提出的方法对于在街道场景中检测植被遮挡是可行的,并表明未知体积的识别对于可靠地解释测量值是必要的。

更新日期:2022-09-20
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