Slope extraction algorithm of ski tracks based on airborne LIDAR point cloud
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摘要: 滑雪场的雪道坡度信息不仅是雪场规划和建设所需的必要内容，也是滑雪运动员提高成绩、减少运动损伤的重要数据。传统的手动测量方式费时费力，且雪场的低温环境不利于测量人员长时间作业。为了解决滑雪场的雪道坡度计算问题，提出了一种基于激光雷达点云的雪道坡度自动提取算法。使用投影高程差滤波、聚类分割等算法对雪场点云进行预处理，获取雪道边缘特征数据。提出了一种自适应阈值的拟合算法，用于雪道中线的提取，该算法可根据点云数量信息及坐标信息，自动选择阈值拟合雪道中线，消除了实际应用中部分点云数据稀疏或缺失对中线拟合带来的不利影响。数据处理结果表明，该方法计算所得坡度值与多次手动测量平均值之间的相对误差为2.2%。这一方法对激光雷达在雪场目标上的应用、雪场测绘中雪道坡度计算等具有参考意义。Abstract: The slope information of ski resort is not only a necessary content for planning and construction of ski resort, but also an important data support for skiers to improve performance and reduce sports injuries. The traditional manual measurement method is time-consuming and the low temperature environment of the ski resort is not conducive to the long-time operation of the surveyors. In order to solve the problem of calculating the slope of ski tracks, an automatic slope extraction algorithm of ski tracks based on LIDAR point cloud was proposed. The projection difference of elevation filtering, clustering segmentation and other algorithms were used to preprocess the ski resort point cloud and obtain the feature data of ski tracks edge. An adaptive threshold fitting algorithm was proposed for the extraction of the center line of ski tracks. This algorithm could automatically select the threshold to fit the center line of ski tracks according to the point cloud quantity information and coordinate information, which eliminated the negative effect of the data sparse or missing of point cloud on the center line fitting. The data processing results show that the relative error between the slope value calculated by this method and the average value of multiple manual measurements is 2.2%. This method is of reference significance to the application of LIDAR in the target of ski resorts and to the calculation of slope of snow tracks in the mapping of ski resorts.
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Keywords:
- LIDAR /
- slope extraction /
- adaptive threshold fitting /
- ski tracks measurement
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图 4 经投影高程差滤波后的非地面点
Figure 4. Non-ground points filtered by projection difference of elevation
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图 8 自动计算与手动测量坡度值对比图
Figure 8. Comparison curves between automatic calculation and manual measurement of slope values
表 1 实验数据测试地点及所用设备
Table 1 Experimental data test site and equipment used
Laser scanning system GPS/IMU High performance camera Unmanned aircraft systems Test site Rigel miniVUX-1DL Applanix APX-15 Sony A6000 micro single camera Dji M600Pro Alpine skiing events at Chongli 
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表 2 算法参数设置规则及可能产生的误差
Table 2 Algorithm parameter setting rules and possible errors
参数 参数设置 误差分析 k邻域半径 大 曲面拟合较为复杂，点特征出现扭曲，边缘的特征以及隐含细节被涂抹 小 法线的方向变化更加多样，更加散乱 最小聚类点数 大 时间消耗会大幅上升，并且可能会出现错误分类的现象 小 出现同一属性点相比于其他近邻点距离较大而未进行判决的问题 搜索邻域点个数 大 目标点云簇的细节会出现缺失 小 可能会出现非目标点误分类的现象 法线差阈值 大 无法将所求的目标点与无关点进行有效分割 小 会出现目标点的漏分类现象
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表 3 部分坡度数据展示
Table 3 Partial slope data
Height/m 1 968 1 969 1 970 1 971 1 972 1 973 1 974 1 975 1 976 Manual calculated/（°） 19.1 19.6 19.9 20.1 19.2 18.2 18.1 20.3 20.3 Automatic calculated/（°） 19.9 19.8 20.7 20.9 19.6 17.3 18.4 20.6 19.7
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