基于rtklib PPK和PPP gnss解决方案的机载激光扫描gnss / imu / lidar集成评估

Q2 Social Sciences
F. Pöppl, G. Mandlburger, N. Pfeifer
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引用次数: 0

摘要

摘要机载激光扫描可以有效地获取大面积的精确3D数据。由于LiDAR数据的地理参考需要了解平台轨迹,因此激光扫描仪系统通常由全球导航卫星系统(GNSS)接收器/天线和惯性测量单元(IMU)组成。标准的处理流程包括GNSS/IMU集成、地理参考和后续的激光数据调整。在这里,我们考虑了一种基于最小二乘平差的GNSS/IMU/ lidar整体集成方法。GNSS是松散耦合的,采用后处理运动学或精确点定位GNSS处理策略,利用开源软件RTKLib获得GNSS位置。在本文中,我们将得到的点云与标准处理工作流程的点云进行了比较,并评估了不同处理策略对机载激光测深(ALB)数据集内部一致性和绝对精度方面的点云质量的影响。虽然GNSS解决方案本身差异很大,但与参考解决方案相比,PPK-和ppp -导出的点云都显示出更好的条带差异(低于2.5 cm)和相似的绝对精度(修正恒定基准位移后参考目标的RMSE为4 cm)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
EVALUATION OF A GNSS/IMU/LIDAR-INTEGRATION FOR AIRBORNE LASER SCANNING USING RTKLIB PPK AND PPP GNSS SOLUTIONS
Abstract. Airborne laser scanning allows for efficient acquisition of accurate 3D data for large areas. Because georeferencing of the LiDAR data requires knowledge of the platform trajectory, the laser scanner system commonly comprises a global navigation satellite system (GNSS) receiver/antenna and an inertial measurement unit (IMU). The standard processing pipeline consists of GNSS/IMU integration, georeferencing, and subsequent adjustment of the laser data. Here, we consider a holistic GNSS/IMU/LiDAR-integration approach based on least-squares adjustment. The GNSS is loosely coupled, and the GNSS positions are obtained using either postprocessing kinematic or precise point positioning GNSS processing strategies using the open-source software RTKLib. In this contribution, we compare the resulting point clouds to those of a standard processing workflow and evaluate the impact of the different processing strategies on point cloud quality in terms of internal consistency and absolute accuracy for a airborne laser bathymetry (ALB) dataset. Although the GNSS solutions themselves differ strongly, both the PPK- and the PPP-derived point clouds show better strip differences (below 2.5 cm) and similar absolute accuracy (<4 cm RMSE w.r.t. reference targets after correction of constant datum shift) compared to the reference solution.
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来源期刊
CiteScore
1.70
自引率
0.00%
发文量
949
审稿时长
16 weeks
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