Reference Lidar Surfaces for Enhanced Aerial Triangulation and Camera Calibration

Abstract

Commission I, WG I/1 KEY WORDS: aerial triangulation, camera calibration, lidar, registration ABSTRACT:Due to the complementary characteristics of lidar and photogrammetry, the integration of data derived from these techniques continues to receive attention from the relevant research communities. The research presented in this paper draws on this by adopting lidar data as a control surface from which aerial triangulation and camera system calibration can be performed. The research methodology implements automatic registration between the reference lidar DTM and dense photogrammetric point clouds which are derived using Integrated Sensing Orientation (ISO). This utilises a robust least squares surface matching algorithm, which is iterated to improve results by increasing the photogrammetric point quality through self-calibrating bundle adjustment. After a successful registration, well distributed lidar control points (LCPs) are automatically extracted from the transformed photogrammetric point clouds using predefined criteria. Finally, self-calibrating bundle block adjustment using different configurations of LCPs is performed to refine camera interior orientation (IO) parameters. The methodology has been assessed using imagery from a Vexcel UltraCamX large format camera. Analysis and the performance of the camera and its impact on the registration accuracy was performed. Furthermore, refinement of camera IO parameters was also applied using the derived LCPs. Tests also included investigations into the influence of the number and weight of LCPs in the accuracy of the bundle adjustment. Results from the UltraCamX block were compared with reference calibration results using ground control points in the test area, with good agreement found between the two approaches.

Digital Elevation Model Differencing and Error Estimation from Multiple Sources: A Case Study from the Meiyuan Shan Landslide in Taiwan

“…However, due to the facts that most landslide areas are often located in areas with high mountains and forests featuring rugged terrain and poor traffic accessibility, with only a limited number of image characteristic points, the representativeness of error assessment might be adversely influenced due to the lack of sufficient ground measurement points, leading to potential underestimation. There are reported studies in which aerotriangulation or aerial photogrammetry operations were conducted by using high-precision LiDAR DEM data as control points or a reference terrain surface [48][49][50]. With the six periods of DTM data generated using various techniques in this study, in order to avoid the impacts of insufficient ground control points on error estimation, the DEM data from 2011 were used as the reference elevation.…”

Automatic sensor orientation using horizontal and vertical line feature constraints

“…To address the limitation of the traditional BA method applied in weak convergence geometry, DEM-aided BA method was proposed by (Teo et al, 2010), in which Digital Elevation Model (DEM) was used as the elevation control. To overcome the need for GCPs required by the traditional BA method, a dense network of reference points were extracted by a least squares surface matching and then used for GCPs (Gneeniss et al, 2013).…”

The Influence of Sub-Block Position on Performing Integrated Sensor Orientation Using In Situ Camera Calibration and Lidar Control Points

“…It was expected that IOP values, obtained from the entire block, would achieve better results for object space coordinates. However, as remarked in [9], four full LCPs, positioned at the corner of block, were recommended to perform this calibration.…”

“…Mitishita et al [19], Gneeniss [9] and Gneeniss et al [11] gave details about the main steps used to perform the in situ calibration using different digital cameras.…”

“…Usually, point features are extracted after defining existing geometric shapes such as buildings' corners or ridge roof corners. Gneeniss [9] presented a complete background about different methods to extract specific features. The approach performed in this research was focused on point-feature extraction by the intersection of three building roof planes.…”