Attitude Mounting Misalignment Estimation Method for the Calibration of UAV LiDAR System by using a TIN-based Corresponding Model

Abstract

In traditional attitude mounting misalignment estimation methods for the calibration of unmanned autonomous vehicle (UAV) based light detection and ranging (LiDAR) system, signalized targets and iterative corresponding models are required, which makes it highly cost and computationally time-consuming. This paper presents an attitude mounting misalignment estimation (AMME) method for the calibration of UAV LiDAR system. The proposed method is divided into the coarse registration of LiDAR strips and the estimation of the attitude mounting misalignment. Firstly, 3D keypoints are extracted in the point clouds using the scale-invariant feature transform (SIFT) algorithm. Afterwards, the point feature transform (PFH) descriptor is used for 3D keypoint matching. Then, the coarse registration is executed. In the second part of the contribution, the systematic errors in the attitude mounting misalignment are estimated by incorporating the proposed triangular irregular network (TIN) corresponding model into the calibration modelling. Using the TIN-based corresponding model saves time and cost for AMME method. Furthermore, it provides two important effects: practical and computational, as no designed calibration boards, segmentation and iterative matching are needed. The performance of the proposed method is demonstrated under an UAV LiDAR data onboarded with lightweight navigation sensors. The experimental results show the efficacy of the method in comparison with a state-of-the-art method.