Abstract
This paper discusses the automatic coregistration and fusion of 3d point clouds generated from aerial image sequences and corresponding thermal infrared (TIR) images. Both RGB and TIR images have been taken from a RPAS platform with a predefined flight path where every RGB image has a corresponding TIR image taken from the same position and with the same orientation with respect to the accuracy of the RPAS system and the inertial measurement unit. To remove remaining differences in the exterior orientation, different strategies for coregistering RGB and TIR images are discussed: (i) coregistration based on 2D line segments for every single TIR image and the corresponding RGB image. This method implies a mainly planar scene to avoid mismatches; (ii) coregistration of both the dense 3D point clouds from RGB images and from TIR images by coregistering 2D image projections of both point clouds; (iii) coregistration based on 2D line segments in every single TIR image and 3D line segments extracted from intersections of planes fitted in the segmented dense 3D point cloud; (iv) coregistration of both the dense 3D point clouds from RGB images and from TIR images using both ICP and an adapted version based on corresponding segmented planes; (v) coregistration of both image sets based on point features. The quality is measured by comparing the differences of the back projection of homologous points in both corrected RGB and TIR images.
Highlights
Today, automatic 3d reconstruction and texture extraction is focused on high resolution images and image sequences from the visual spectrum
A geometric calibration including principal point, focal length, and radial distortion parameters has been investigated by some groups (Simmler, 2009; Luhmann et al, 2010; Lagela et al, 2011). 3d reconstruction and texture extraction in thermal infrared are applied for sets of images and ordered terrestrial image sequences (Hoegner and Stilla, 2015) or image sequences taken by a thermal camera mounted on a RPAS (Westfeld et al, 2015)
As the RGB point cloud is very dense compared to the thermal infrared (TIR) images based point cloud, for every 3D point of the TIR point cloud the minimum distance to a point in the RGB point cloud is determined as it is assumed that the discretization errors in the position of the 3D points are small in the RGB point cloud compared to the 3D points in the TIR point cloud
Summary
Automatic 3d reconstruction and texture extraction is focused on high resolution images and image sequences from the visual spectrum. 3d reconstruction and texture extraction in thermal infrared are applied for sets of images and ordered terrestrial image sequences (Hoegner and Stilla, 2015) or image sequences taken by a thermal camera mounted on a RPAS (Westfeld et al, 2015). This paper concentrates on a set of methods to coregister and fuse images taken with both a RGB and a thermal infrared (TIR) camera mounted on a RPAS. Instead of mounting both systems together with a fixed, calibrated relative orientation, here both cameras record the scene one after another in two fights following the same predefined flight path.
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