Abstract
Abstract. The Laser-IMU boresight calibration is the precondition for an Unmanned Aerial Vehicle (UAV)-Light Detection and Ranging (LiDAR) system (ULS). The existing methods achieve good performance for calibrating ULSs with high-precision positioning and orientation systems (POS) (e.g., APX-15), in which, the systematic errors of the high-precision POS can be ignored, only the boresight parameters are estimated. However, these methods have difficulties in calibrating the low-cost ULSs with low-precision POS. To overcome the impact of the systematic errors of the low-precision POS on boresight calibration, an aerial-triangulation aided boresight calibration is proposed in this paper. It simultaneously estimates the laser-IMU boresight angles and system states (e.g. trajectory) by setting the point clouds derived from aerial-triangulation (AT point clouds) as the reference. Firstly, the planar voxels from the AT point clouds are extracted, due to the fact that they are more reliable in AT point clouds. Secondly, raw laser observations are matched with the extracted planar voxels to establish laser matching observations. Thirdly, a Dynamic Network (DN) is built using the GNSS observations, inertial observations, and laser matching observations to simultaneously optimize the initial laser-IMU boresight angles and the system states. All the sensor observations involved in the ULS are modeled with proper error models, which are essential for analyzing and refining the data quality of the low-cost ULS. The proposed method was tested to calibrate a low-cost ULS, KylinCloud-II, in a calibration field. It showed that the average distance between the laser point clouds and the referenced AT point clouds was decreased from 2.560m (RMSE = 3.88m) to 0.08m (RMSE = 0.99m).
Highlights
Unmanned Aerial Vehicle (UAV)-Light Detection and Ranging (LiDAR) Systems (ULS) are widely used in many applications, such as building information modeling (Roca et al, 2014), urban changes detection (Qin et al, 2016), power-line corridor inspection (Chen et al, 2018), forest inventory estimation (Jaakkola et al, 2010; Li et al, 2019b; Liu et al, 2018; Wallace et al, 2012)
The voxelization of the AT point clouds with planarity was illustrated in Fig.6 (b)
After applying the values obtained by the proposed method, the average distance is reduced to 0.08 m (RMSE = 0.99 m)
Summary
Unmanned Aerial Vehicle (UAV)-Light Detection and Ranging (LiDAR) Systems (ULS) are widely used in many applications, such as building information modeling (Roca et al, 2014), urban changes detection (Qin et al, 2016), power-line corridor inspection (Chen et al, 2018), forest inventory estimation (Jaakkola et al, 2010; Li et al, 2019b; Liu et al, 2018; Wallace et al, 2012). The trajectory errors of the low-cost ULSs equipped low-end POSs can not be ignored, posing difficulties for the existing calibration methods This problem was partly solved by integrating a camera in a ULS to improve the trajectory accuracies with the aid of aerial-triangulation (Elbahnasawy et al, 2018; Li et al, 2019a). An aerial-triangulation aided boresight calibration method is proposed to calibrate the laser-IMU boresight angles for a low-cost ULS equipped with low-end POS. Different from the existing methods, the proposed method considers the systematic error of the low-end POS, and estimates the laserIMU boresight and system states simultaneously In this manner, the sensors' error models are properly modeled, which are essential for analyzing and refining the data quality of the lowcost ULS.
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