Abstract

Advances in micro-electro-mechanical navigation systems and lightweight LIDAR (light detection and ranging) sensors onboard unmanned aerial vehicles (UAVs) provide the feasibility of deriving point clouds with very high and homogeneous point density. However, the deformations caused by numerous sources of errors should be carefully treated. This work presents a rigorous calibration of UAV-based LiDAR systems with refinement of the boresight angles using a point-to-plane approach. Our method is divided into a calibration and a parameter mounting refinement part. It starts with the estimation of the calibration parameters and then refines the boresight angles. The novel contribution of the paper is two-fold. First, we estimate the calibration parameters conditioning the centroid of a plane segmented to lie on its corresponding segmented plane without an additional surveying campaign. Second, we refine the boresight angles using a new point-to-plane model. The proposed method is evaluated by analyzing the accuracy assessment of the adjusted point cloud to point/planar features before and after the proposed method. Compared with the state-of-the-art method, our proposed method achieves better positional accuracy.

Highlights

  • Nowadays, unmanned aerial vehicles (UAVs) based on LiDAR systems are one of the most cost-effective tools for a broad class of different applications such as digital building model generation [1], mapping [2], disaster management [3,4], forestry inventory [5,6,7], archaeological studies [8,9], power line inspection [10], and others

  • This study focuses on the rigorous calibration of UAV-based LiDAR systems, investigating a point-to-plane strategy that is capable of estimating the calibration parameters and refining the boresight angles to obtain an accurate 3D point cloud

  • The objects were classified on the ground and non-ground points using a progressive morphological filter (PPF), in which only the buildings were preserved

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Summary

Introduction

UAVs (unmanned aerial vehicles) based on LiDAR (light detection and ranging) systems are one of the most cost-effective tools for a broad class of different applications such as digital building model generation [1], mapping [2], disaster management [3,4], forestry inventory [5,6,7], archaeological studies [8,9], power line inspection [10], and others. Due to their flexibility and mobility, they have great potential for mapping and change detection. There is no easy way to model such effects and to remove them from observations without calibrating the role of the system

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