基于三维激光点云的树木胸径自动提取方法

胸径是评价林木生长状况的重要参数之一。针对接触式人工测量自动化程度低和基于点云的现有算法提取树木胸径精度不高的问题,提出一种基于点云数据的自动准确获取树木胸径的新方法。该方法以树木点云数据为基础,运用蚁群算法和B样条曲线拟合技术,实现树木胸径的自动准确提取。对实验区树木测量计算,结果表明,利用该方法提取树木胸径的均方根误差为±0.19 cm,平均绝对误差为0.15 cm,相对于基于点云的传统算法提取精度分别提高了50%和60.7%。该方法基于高精度点云数据,实现了树木胸径的无损自动提取,在精准林业领域具有推广价值。

Automatic extraction method for tree diameter at breast height measurement based on 3D point cloud

The diameter at breast height (DBH) is one of the important parameters to evaluate the growth of forest trees. In view of the low degree of automation of contact-type artificial field measurement, and the low accuracy of existing algorithms based on point cloud, a new method using 3D laser scanning technology to automatically measure tree DBH was proposed. Based on tree point cloud data, using ant colony algorithm and spline curve fitting technique, the DBH was measured by programming automatically. Sample trees in the experimental area were measured and calculated, and the results showed that the root-mean-square error of the algorithm for the extraction of tree diameter was
0.19 cm, and the average-absolute error was 0.15 cm, which were improved by 50% and 60.7%, respectively compared with the traditional algorithm based on point cloud. Based on high-precision point cloud data, this method can achieve the automatic extraction of tree diameter at breast height and showed potential to be popularized in the field of precision forestry.