The Lidargrammetric Model Deformation Method for Altimetric UAV-ALS Data Enhancement

Abstract

The altimetric accuracy of aerial laser scanning (ALS) data is one of the most important issues of ALS data processing. In this paper, the authors present a previously unknown, yet simple and efficient method for altimetric enhancement of ALS data based on the concept of lidargrammetry. The generally known photogrammetric theory of stereo model deformations caused by relative orientation parameters errors of stereopair was applied for the continuous correction of lidar data based on ground control points. The preliminary findings suggest that the method is correct, efficient and precise, whilst the correction of the point cloud is continuous. The theory of the method and its implementation within the research software are presented in the text. Several tests were performed on synthetic and real data. The most significant results are presented and discussed in the article together with a discussion of the potential of lidargrammetry, and the main directions of future research are also mapped out. These results confirm that the research gap in the area of altimetric enhancement of ALS data without additional trajectory data is resolved in this study.

Comparative analysis of surface deformation monitoring in a mining area based on UAV‐lidar and UAV photogrammetry

“…Most researchers have achieved superior results in the combination of measurement techniques (Liu et al., 2023; Markiewicz et al., 2022; Stothard & Shirani Faradonbeh, 2023). Additionally, some have enhanced the accuracy of low‐precision lidar through the utilisation of photogrammetric techniques (Li et al., 2019; Rzonca & Twardowski, 2022; Yuan et al., 2022), or conversely, improved photogrammetric accuracy through different lidar applications (Terpstra et al., 2018, 2019). The challenge persists in merging data from both techniques to enhance monitoring accuracy, particularly when the monitoring precision of both techniques is within the centimetre range (within 10 cm).…”

The Utilization of Synthetic and Semisynthetic Point Clouds and Images for Testing Novel Approaches for Correcting Lidar Data

“…Another advantage of this way is that synthetic and semisynthetic benchmark data seems to be used easier for automatic testing than real data, because the results are more evident according to the testing data preparation (Rzonca andTwardowski, 2022, Wang et al, 2019).…”