Access to highly detailed models of heterogeneous forests from the near surface to above the tree canopy at varying scales is of increasing demand as it enables more advanced computational tools for analysis, planning, and ecosystem management. LiDAR sensors available through different scanning platforms including terrestrial, mobile and aerial have become established as one of the primary technologies for forest mapping due to their inherited capability to collect direct, precise and rapid 3D information of a scene. However, their scalability to large forest areas is highly dependent upon use of effective and efficient methods of co-registration of multiple scan sources. Surprisingly, work in forestry in GPS denied areas has mostly resorted to methods of co-registration that use reference based targets (e.g., reflective, marked trees), a process far from scalable in practice. In this work, we propose an effective, targetless and fully automatic method based on an incremental co-registration strategy matching and grouping points according to levels of structural complexity. Empirical evidence shows the method's effectiveness in aligning both TLS-to-TLS and TLS-to-ALS scans under a variety of ecosystem conditions including pre/post fire treatment effects, of interest to forest inventory surveyors.

翻译：随着高度精细的异质森林模型（从近地表到树冠层以上不同尺度）的需求日益增长，这种模型能够支持更高级的计算工具用于分析、规划和生态系统管理。基于不同扫描平台（包括地面、移动和航空平台）的LiDAR传感器，因其能够直接、精确且快速获取场景的三维信息，已成为森林测绘的主要技术之一。然而，其向大面积森林区域的可扩展性高度依赖于对多源扫描数据有效且高效配准方法的使用。令人惊讶的是，在GPS受限区域的林业研究中，大多沿用基于参考目标（如反射标识、标记树木）的配准方法，此过程在实际应用中可扩展性极差。本文提出一种有效、无目标且全自动的增量式配准方法，根据结构复杂度层级匹配并分组点云。实证结果表明，该方法在多种生态系统条件下（包括火灾前后处理效应，这对森林资源调查员具有重要价值）对地面-地面扫描和地面-航空扫描数据均具有有效的配准能力。