Abstract
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.
Highlights
With the development of Mobile Laser Scanning (MLS) technologies, detailed forest inventories based on Light Detection and Ranging (LiDAR) have become more practical than before
Positioning and mapping results are evaluated with various positioning solutions including Global Navigation Satellite Systems (GNSS) only, GNSS + Inertial Measurement Units (IMU), and the proposed Simultaneous Localization and Mapping (SLAM) + IMU
Since the true trajectory of the field test cannot be directly measured by the on-board georeference sensor, the positioning trajectories of GNSS + IMU and SLAM + IMU solutions are indirectly evaluated with the aforementioned reference object networks consisting of the coordinates of 224 trees along the driving route whose locations are derived from total station measurements
Summary
With the development of Mobile Laser Scanning (MLS) technologies, detailed forest inventories based on Light Detection and Ranging (LiDAR) have become more practical than before. Using Differential GPS (DGPS) or Real-Time Kinematic (RTK)-capable receivers is an option These high-precision positioning methods, which work correctly in open sky environments, suffer severe signal loss in forests, where dense canopy absorbs, reflects or completely blocks the GNSS radio frequency (RF) signal, leading to degraded positioning results with errors of up to tens of meters. Such signal loss greatly hinders their capability to determine a reliable position in forests with GNSS-only positioning solutions [7]. The lack of accurate and reliable positioning solutions in mature and dense forest environments highly restricts the level of automation
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