Abstract
Terrestrial laser scanning (TLS) is the standard technique used to create accurate point clouds for digital forest inventories. However, the measurement process is demanding, requiring up to two days per hectare for data collection, significant data storage, as well as resource-heavy post-processing of 3D data. In this work, we present a real-time mapping and analysis system that enables online generation of forest inventories using mobile laser scanners that can be mounted e.g. on mobile robots. Given incrementally created and locally accurate submaps-data payloads-our approach extracts tree candidates using a custom, Voronoi-inspired clustering algorithm. Tree candidates are reconstructed using an adapted Hough algorithm, which enables robust modeling of the tree stem. Further, we explicitly incorporate the incremental nature of the data collection by consistently updating the database using a pose graph LiDAR SLAM system. This enables us to refine our estimates of the tree traits if an area is revisited later during a mission. We demonstrate competitive accuracy to TLS or manual measurements using laser scanners that we mounted on backpacks or mobile robots operating in conifer, broad-leaf and mixed forests. Our results achieve RMSE of 1.93 cm, a bias of 0.65 cm and a standard deviation of 1.81 cm (averaged across these sequences)-with no post-processing required after the mission is complete.
Abstract (translated)
地面激光扫描（TLS）是创建数字森林清查的准确点云的标准技术。然而，测量过程要求较高，需要每公顷数据收集长达两天的开销，以及处理大量3D数据的资源密集型后处理。在这项工作中，我们提出了一个可以在线生成森林清查的移动激光扫描器系统，该系统可以通过安装在移动机器人上实现。鉴于通过逐步创建的局部准确子图数据负载，我们的方法使用自定义的Voronoi启发式聚类算法提取树候选者。树候选者通过适应的Hough算法进行重建，该算法可以稳健地建模树干。此外，我们通过使用姿态图LiDAR SLAM系统更新数据库，明确表示数据的增益性。这使我们能够在后续任务中重新访问该区域时，优化我们对树特征的估计。我们在 conifer、 broad-leaf 和 mixed 森林中安装的激光扫描器上进行了竞争性的 TLS 或手动测量。我们的结果在平均这些序列中的 RMSE、偏差和标准差分别为 1.93 cm、0.65 cm 和 1.81 cm 的情况下，实现了卓越的准确度。在任务完成后的不需要后处理。
URL
https://arxiv.org/abs/2403.17622
