A Strip Adjustment Method of UAV-Borne LiDAR Point Cloud Based on DEM Features for Mountainous Area

Abstract

Due to the trajectory error of the low-precision position and orientation system (POS) used in unmanned aerial laser scanning (ULS), discrepancies usually exist between adjacent LiDAR (Light Detection and Ranging) strips. Strip adjustment is an effective way to eliminate these discrepancies. However, it is difficult to apply existing strip adjustment methods in mountainous areas with few artificial objects. Thus, digital elevation model-iterative closest point (DEM-ICP), a pair-wise registration method that takes topography features into account, is proposed in this paper. First, DEM-ICP filters the point clouds to remove the non-ground points. Second, the ground points are interpolated to generate continuous DEMs. Finally, a point-to-plane ICP algorithm is performed to register the adjacent DEMs with the overlapping area. A graph-based optimization is utilized following DEM-ICP to estimate the correction parameters and achieve global consistency between all strips. Experiments were carried out using eight strips collected by ULS in mountainous areas to evaluate the proposed method. The average root-mean-square error (RMSE) of all data was less than 0.4 m after the proposed strip adjustment, which was only 0.015 m higher than the result of manual registration (ground truth). In addition, the plane fitting accuracy of lateral point clouds was improved 4.2-fold, from 1.565 to 0.375 m, demonstrating the robustness and accuracy of the proposed method.

Feasibility evaluation of radiotherapy positioning system guided by augmented reality and point cloud registration

“…Finally, the objective function E (T) is minimized on the corresponding set K to complete registration. The E(T) of Point‐to‐Plane ICP is shown in the following Equation () 14 $$$$\begin{equation}{\mathrm{E}}\left( {\mathrm{T}} \right)\ = \mathop \sum \limits_{\left( {{\mathrm{p}},{\mathrm{q}}} \right)\ } {\left( {\left( {{\mathrm{p}} - {{\mathrm{T}}}_{\mathrm{q}}} \right) \cdot {{\mathrm{n}}}_{\mathrm{p}}} \right)}^2)\ \ \end{equation}$$$$Where p and q are two‐points belonging to K and n p is the normal of point p. p and q were from two‐point clouds that need to be registered, respectively.…”

“…Finally, the objective function E (T) is minimized on the corresponding set K to complete registration. The E(T) of Point‐to‐Plane ICP is shown in the following Equation ( 2 ) 14 Where p and q are two‐points belonging to K and n p is the normal of point p. p and q were from two‐point clouds that need to be registered, respectively.…”

An Accuracy Assessment of Snow Depth Measurements in Agro-Forested Environments by UAV Lidar

“…Another method to reduce random errors caused by GNSS and IMU is the strip adjustment, which fundamentally decreases the discrepancies between flight strips. The strip adjustment (or strip alignment) technique has proven to be very successful with ALS data [35,36], and implementing it on UAV data remains an active area of research [21,37]. One of the reasons that this technique is not (yet) very popular among UAV users is that the readily available strip adjustment algorithms require raw data of the laser scanner, which is often not accessible for most end-users through the UAV lidar system [21,32].…”

An Error Prediction Model for Construction Bulk Measurements Using a Customized Low-Cost UAS-LIDAR System

“…In practice, the observed error magnitude and pattern is related to the target application as well. For example, errors have been assessed for forestry [45], meadow steppe [46], mountainous areas [47], flood plains [48], and different vegetation levels [49]. The focus of this work is on the vertical error on bulk measurements, such as piles or excavation.…”