SLAM-Aided Stem Mapping for Forest Inventory with Small-Footprint Mobile LiDAR
SLAM-Aided Stem Mapping for Forest Inventory with Small-Footprint Mobile LiDAR
复制标题
使用小型移动 LiDAR 进行 SLAM 辅助树干测绘，进行森林清查
DOI：
10.3390/f6124390
复制
发表时间：
2015-12-01
期刊：
影响因子：
2.9
通讯作者：
Hyyppa, Hannu
中科院分区：
文献类型：
--
作者：
Tang, Jian;Chen, Yuwei;Hyyppa, Hannu
准确反演树干位置分布是森林资源清查生物量估算的基本要求。将惯性测量单元（IMU）与全球导航卫星系统（GNSS）相结合是大多数移动的激光扫描（MLS）系统中用于精确森林测绘的常用定位策略。与战术级或消费级IMU相结合，GNSS在开阔的森林环境中提供了令人满意的解决方案，可以实现优于1分米的定位精度。然而，对于这样的MLS系统，在成熟和茂密的森林中定位仍然是一项具有挑战性的任务，因为GNSS信号被厚厚的树冠衰减而丢失。MLS系统中的激光扫描传感器通常用于测绘和建模，而不是定位。在本文中，我们研究了一个同时定位和地图（SLAM）辅助定位解决方案与点云收集的小足迹激光雷达。基于实地测试数据，我们评估了SLAM定位和制图在森林资源清查中的潜力。结果表明，在选定的测试领域的定位精度提高了38%，相比传统的战术级IMU + GNSS定位系统在成熟的森林环境，因此，我们能够产生一个明确的树木分布图。
Accurately retrieving tree stem location distributions is a basic requirement for biomass estimation of forest inventory. Combining Inertial Measurement Units (IMU) with Global Navigation Satellite Systems (GNSS) is a commonly used positioning strategy in most Mobile Laser Scanning (MLS) systems for accurate forest mapping. Coupled with a tactical or consumer grade IMU, GNSS offers a satisfactory solution in open forest environments, for which positioning accuracy better than one decimeter can be achieved. However, for such MLS systems, positioning in a mature and dense forest is still a challenging task because of the loss of GNSS signals attenuated by thick canopy. Most often laser scanning sensors in MLS systems are used for mapping and modelling rather than positioning. In this paper, we investigate a Simultaneous Localization and Mapping (SLAM)-aided positioning solution with point clouds collected by a small-footprint LiDAR. Based on the field test data, we evaluate the potential of SLAM positioning and mapping in forest inventories. The results show that the positioning accuracy in the selected test field is improved by 38% compared to that of a traditional tactical grade IMU + GNSS positioning system in a mature forest environment and, as a result, we are able to produce a unambiguous tree distribution map.