基于拓检查和LiDAR点云边界分析的街道树的层次优化细分和参数提取
Yuan Kou1,2, Xianjun Gao3,4,5, Yue Zhang3
1The First Surveying and Mapping Institute of Hunan Province, Changsha 410114, China.
Sensors (Basel, Switzerland)
|January 11, 2025
概括
这项研究引入了一种新的算法,用于从LiDAR点云对路边树进行细分,从而提高自动驾驶应用的准确性. 该方法有效地处理缺失的数据,达到99%以上的个体树分段精度.
科学领域:
- 计算机视觉 计算机视觉
- 地理空间分析是什么
- 自主系统 自主系统
背景情况:
- 准确的路边树木细分对于虚拟道路场景模拟和自动驾驶至关重要.
- 移动激光扫描可以引入点云缺陷,导致低分段和过分分段问题.
- 现有的方法很难满足对个体树木细分的高精度需求.
研究的目的:
- 开发一个强大的路边树分段算法,解决常见的分段错误.
- 为了能够精确地提取树的参数,如高度,胸高度的直径和冠面积.
- 为了提高点云数据对辅助自动驾驶的可靠性.
主要方法:
- 使用无监督聚类进行场景预分割.
- 通过投影拓检查优化细分.
- 适应性voxel边界分析用于改进细分边界.
- 在分割后提取关键树参数.
主要成果:
- 在测试的路边场景中有效识别所有树木.
- 实现了平均99.07%的个体树分段精度.
- 关键树度指标的参数提取精度超过了90%.
结论:
- 拟议的算法显著提高了路边树木细分的准确性.
- 该方法为虚拟模拟和自动驾驶系统提供可靠的数据.
- 精确的参数提取支持详细的环境建模和分析.
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