Registering point clouds of forest environments is an essential prerequisite for LiDAR applications in precision forestry. State-of-the-art methods for forest point cloud registration require the extraction of individual tree attributes, and they have an efficiency bottleneck when dealing with point clouds of real-world forests with dense trees. We propose an automatic, robust, and efficient method for the registration of forest point clouds. Our approach first locates tree stems from raw point clouds and then matches the stems based on their relative spatial relationship to determine the registration transformation. In contrast to existing methods, our algorithm requires no extra individual tree attributes and has linear complexity to the number of trees in the environment, allowing it to align point clouds of large forest environments. Extensive experiments have revealed that our method is superior to the state-of-the-art methods regarding registration accuracy and robustness, and it significantly outperforms existing techniques in terms of efficiency. Besides, we introduce a new benchmark dataset that complements the very few existing open datasets for the development and evaluation of registration methods for forest point clouds. The source code of our method and the dataset are available at https://github.com/zexinyang/AlignTree.
翻译:森林环境的点云登记是精密林业中LIDAR应用的基本先决条件。森林点云登记的最新方法要求提取各个树的特性,在用茂密的树木处理真实世界森林的点云时,它们有一个效率瓶颈。我们提出了一种自动、稳健和有效的森林点云登记方法。我们的方法首先定位树木,从原始点云中找到树,然后根据它们的相对空间关系将根子匹配起来,以确定登记转换。与现有方法不同,我们的算法不需要额外的个别树属性,并且对环境树木数量具有线性复杂性,使其能够对大森林环境的点云进行调和。广泛的实验表明,我们的方法优于登记准确性和稳健性方面的最先进方法,大大超出现有的效率技术。此外,我们引入了一个新的基准数据集,以补充现有的为数不多的森林点云登记方法的开发和评估开放数据集。我们方法的来源代码和数据集可在 https://githhub.com/zexinyang/AlignT查阅。