Abstract

Point cloud rectification is an efficient approach to improve the quality of laser point cloud data. Conventional rectification methods mostly relied on ground control points (GCPs), typical artificial ground objects, and raw measurements of the laser scanner which impede automation and adaptability in practice. This paper proposed an automated rectification method for the point cloud data that are acquired by an unmanned aerial vehicle LiDAR system based on laser intensity, with the goal to reduce the dependency of ancillary data and improve the automated level of the rectification process. First, laser intensity images were produced by interpolating the intensity data of all the LiDAR scanning strips. Second, a scale-invariant feature transform algorithm was conducted to extract two dimensional (2D) tie points from the intensity images; the pseudo tie points were removed by using a random sample consensus algorithm. Next, all the 2D tie points were transformed to three dimensional (3D) point cloud to derive 3D tie point sets. After that, the observation error equations were created with the condition of coplanar constraints. Finally, a nonlinear least square algorithm was applied to solve the boresight angular error parameters, which were subsequently used to correct the laser point cloud data. A case study in Shehezi, Xinjiang, China was implemented with our proposed method and the results indicate that our method is efficient to estimate the boresight angular error between the laser scanner and inertial measurement unit. After applying the results of the boresight angular error solution to rectify the laser point cloud, the planar root mean square error (RMSE) is 5.7 cm and decreased by 1.1 cm in average; the elevation RMSE is 1.4 cm and decreased by 0.8 cm in average. Comparing with the stepwise geometric method, our proposed method achieved similar horizontal accuracy and outperformed it in vertical accuracy of registration.

Highlights

  • Unmanned aerial vehicle (UAV) Light Detection and Ranging (LiDAR) is a new technology in the field of survey and mapping that is equipped with low-altitude UAV platform for LiDAR data acquisition and composed of three core components, including laser measurement, differential Global Navigation Satellite System (GNSS), and inertial navigation unit (IMU) [1,2]

  • A total of 63 pairs of tie points were extracted with the Scale-Invariant Feature Transform (SIFT) algorithm and 23 pairs of refined tie points were remained after removing pseudo matching points

  • This paper presents a new method for the boresight angular error rectification of a UAV LiDAR system based on the laser intensity information

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Summary

Introduction

Unmanned aerial vehicle (UAV) Light Detection and Ranging (LiDAR) is a new technology in the field of survey and mapping that is equipped with low-altitude UAV platform for LiDAR data acquisition and composed of three core components, including laser measurement, differential Global Navigation Satellite System (GNSS), and inertial navigation unit (IMU) [1,2]. Comparing with conventional aerial photogrammetry techniques, UAV LiDAR bears a number of advantages such as being less impacted by flying conditions (e.g., cloud cover and flexible ground control), high-level automation, more precision and density data, and high flexibility. It has been widely used in acquiring digital elevation model [3,4,5], disaster monitoring [6], heritage protection [7], forestry survey [8,9,10,11], and 3D modeling [12]. It is currently a key step to explore reliable, intelligent, and efficient methods for error rectification to improve the quality of laser point cloud data that are captured by UAV LiDAR systems [2]

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