ABSTRACT:The recording of high resolution point clouds with sub-mm resolution is a demanding and cost intensive task, especially with current equipment like handheld laser scanners. We present an image based approached, where techniques of image matching and dense surface reconstruction are combined with a compact and affordable rig of off-the-shelf industry cameras. Such cameras provide high spatial resolution with low radiometric noise, which enables a one-shot solution and thus an efficient data acquisition while satisfying high accuracy requirements. However, the largest drawback of image based solutions is often the acquisition of surfaces with low texture where the image matching process might fail. Thus, an additional structured light projector is employed, represented here by the pseudo-random pattern projector of the Microsoft Kinect. Its strong infrared-laser projects speckles of different sizes. By using dense image matching techniques on the acquired images, a 3D point can be derived for almost each pixel. The use of multiple cameras enables the acquisition of a high resolution point cloud with high accuracy for each shot. For the proposed system up to 3.5 Mio. 3D points with sub-mm accuracy can be derived per shot. The registration of multiple shots is performed by Structure and Motion reconstruction techniques, where feature points are used to derive the camera positions and rotations automatically without initial information.

Results from an in situ analysis of ablative heat shield materials based on stereoscopic photogrammetry during plasma wind tunnel testing are presented. A carbon fiber preform material sample has been investigated in a highenthalpy airflow corresponding to a Hayabusa reentry condition at a 78 km altitude. The applied photogrammetry uses two digital single lens reflex cameras triggered at 3 frames∕s to acquire images simultaneously from two different perspectives of the sample exposed to the high-speed flow. The photogrammetric analysis resolves the surface with 25;000 px∕cm 2 , approximately 400 dpi, allowing the in situ analysis of the recession phenomena of ablators. Material inhomogeneities are identified during the ablation process, and surface recession is derived with an accuracy of 21 μm. The method is a useful tool for the investigation of temporally resolved volumetric ablation phenomena in low-density carbon phenolic materials.

ABSTRACT:Bundle adjustment based on collinearity is the most widely used optimization method within image based scene reconstruction. It incorporates observed image coordinates, exterior and intrinsic camera parameters as well as object space coordinates of the observed points. The latter dominate the resulting nonlinear system, in terms of the number of unknowns which need to be estimated. In order to reduce the size of the problem regarding memory footprint and computational effort, several approaches have been developed to make the process more efficient, e.g. by exploitation of sparsity or hierarchical subdivision. Some recent developments express the bundle problem through epipolar geometry and scale consistency constraints which are free of object space coordinates. These approaches are usually referred to as structureless bundle adjustment. The number of unknowns in the resulting system is drastically reduced. However, most work in this field is focused on optimization towards speed and considers calibrated cameras, only. We present our work on structureless bundle adjustment, focusing on precision issues as camera calibration and residual weighting. We further investigate accumulation of constraint residuals as an approach to decrease the number of rows of the Jacobian matrix.

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ABSTRACT:Photogrammetric data capture of complex 3D objects using UAV imagery has become commonplace. Software tools based on algorithms like Structure-from-Motion and multi-view stereo image matching enable the fully automatic generation of densely meshed 3D point clouds. In contrast, the planning of a suitable image network usually requires considerable effort of a human expert, since this step directly influences the precision and completeness of the resulting point cloud. Planning of suitable camera stations can be rather complex, in particular for objects like buildings, bridges and monuments, which frequently feature strong depth variations to be acquired by high resolution images at a short distance. Within the paper, we present an automatic flight mission planning tool, which generates flight lines while aiming at camera configurations, which maintain a roughly constant object distance, provide sufficient image overlap and avoid unnecessary stations. Planning is based on a coarse Digital Surface Model and an approximate building outline. As a proof of concept, we use the tool within our research project MoVEQuaD, which aims at the reconstruction of building geometry at sub-centimetre accuracy.