LiDAR Scanning with Supplementary UAV Captured Images for Structural Inspections

Structural assessment using remote sensing technologies can be performed efficiently and effectively using such technologies as LiDAR (light detection and ranging). LiDAR can be employed for various structural assessments, such as as-built conditions for a newly constructed facility, routine inspection during its service life, or structural collapse evaluation after a natural hazard or extreme event. However, the main disadvantage of LiDAR is that it is a line-of-sight technology that can result in significant occlusions. Architectural or structural components can be partially or fully occluded by another object with respect to the location of the laser scanner. Supplemental photogrammetry techniques, such as structure from motion (SfM), can be introduced into the workflow to reduce the occlusion in the final result. Since high-resolution cameras have the ability to be mounted on unmanned aerial vehicles (UAVs), typical areas of occlusion associated with ground-based LiDAR and supported structural coverings (e.g. roof or bridge deck) can be reconstructed. In this approach, aerial SfM is selected due to the low investment and operational costs in comparison to airborne LiDAR. This paper demonstrates the techniques and results of both LiDAR and aerial SfM for a case study building. Images captured with a UAV supplement the collected LiDAR and allow for a holistic scene reconstruction. The benefits of deployment of a combined remote sensing platform, such as this, are demonstrated in the case of reconnaissance in the aftermath of extreme events

Accurate automatic localization of surfaces of revolution for self-calibration and metric reconstruction

In this paper, we address the problem of the automatic metric reconstruction Surface of Revolution (SOR) from a single uncalibrated view. The apparent contour and the visible portions of the imaged SOR cross sections are extracted and classified. The harmonic homology that models the image projection of the SOR is also estimated. The special care devoted to accuracy and robustness with respect to outliers makes the approach suitable for automatic camera calibration and metric reconstruction from single uncalibrated views of a SOR. Robustness and accuracy are obtained by embedding a graph-based grouping strategy (Euclidean Minimum Spanning Tree) into an Iterative Closest Point framework for projective curve alignment at multiple scales. Classification of SOR curves is achieved through a 2-dof voting scheme based on a pencil of conics novel parametrization. The main contribution of this work is to extend the domain of automatic single view reconstruction from piecewise planar scenes to scenes including curved surfaces, thus allowing to create automatically realistic image models of man-made objects. Experimental results with real images taken from the internet are reported, and the effectiveness and limitations of the approach are discussed

Real-time robust estimation of vanishing points through nonlinear optimization

Vanishing points are elements of great interest in the computer vision field, since they are the main source of information about the geometry of the scene and the projection process associated to the camera. They have been studied and applied during decades for plane rectification, 3D reconstruction, and mainly auto-calibration tasks. Nevertheless, the literature lacks accurate online solutions for multiple vanishing point estimation. Most strategies focalize on the accuracy, using highly computational demanding iterative procedures. We propose a novel strategy for multiple vanishing point estimation that finds a trade-off between accuracy and efficiency, being able to operate in real time for video sequences. This strategy takes advantage of the temporal coherence of the images of the sequences to reduce the computational load of the processing algorithms while keeping a high level of accuracy due to an optimization process. The key element of the approach is a robust scheme based on the MLESAC algorithm, which is used in a similar way to the EM algorithm. This approach ensures robust and accurate estimations, since we use the MLESAC in combination with a novel error function, based on the angular error between the vanishing point and the image features. To increase the speed of the MLESAC algorithm, the selection of the minimal sample sets is substituted by a random sampling step that takes into account temporal information to provide better initializations. Besides, for the sake of flexibility, the proposed error function has been designed to work using as image features indiscriminately gradient-pixels or line segments. Hence, we increase the range of applications in which our approach can be used, according to the type of information that is available. The results show a real-time system that delivers real-time accurate estimations of multiple vanishing points for online processing, tested in moving camera video sequences of structured scenarios, both indoors and outdoors, such as rooms, corridors, facades, roads, etc

Vision-Based Building Seismic Displacement Measurement by Stratification of Projective Rectification Using Lines

We propose a new flexible technique for accurate vision-based seismic displacement measurement of building structures via a single non-stationary camera with any perspective view. No a priori information about the camera’s parameters or only partial knowledge of the internal camera parameters is required, and geometric constraints in the world coordinate system are employed for projective rectification in this research. Whereas most projective rectifications are conducted by specifying the positions of four or more fixed reference points, our method adopts a stratified approach to partially determine the projective transformation from line-based geometric relationships on the world plane. Since line features are natural and plentiful in a man-made architectural building environment, robust estimation techniques for automatic projective/affine distortion removal can be applied in a more practical way. Both simulations and real-recorded data were used to verify the effectiveness and robustness of the proposed method. We hope that the proposed method could advance the consumer-grade camera system for vision-based structural measurement one more step, from laboratory environments to real-world structural health monitoring systems

An Analysis of Camera Calibration for Voxel Coloring Including the Effect of Calibration on Voxelization Errors

This thesis characterizes the problem of relative camera calibration in the context of three-dimensional volumetric reconstruction. The general effects of camera calibration errors on different parameters of the projection matrix are well understood. In addition, calibration error and Euclidean world errors for a single camera can be related via the inverse perspective projection. However, there has been little analysis of camera calibration for a large number of views and how those errors directly influence the accuracy of recovered three-dimensional models. A specific analysis of how camera calibration error is propagated to reconstruction errors using traditional voxel coloring algorithms is discussed. A review of the Voxel coloring algorithm is included and the general methods applied in the coloring algorithm are related to camera error. In addition, a specific, but common, experimental setup used to acquire real-world objects through voxel coloring is introduced. Methods for relative calibration for this specific setup are discussed as well as a method to measure calibration error. An analysis of effect of these errors on voxel coloring is presented, as well as a discussion concerning the effects of the resulting world-space error

НОРМАЛИЗАЦИЯ ИЗОБРАЖЕНИЙ ОТНОСИТЕЛЬНО ПЕРСПЕКТИВНОГО ПРЕОБРАЗОВАНИЯ НА ОСНОВЕ ГЕОМЕТРИЧЕСКИХ ПАРАМЕТРОВ

Рассматриваются свойства, способ определения и применения перспективного (плоскостнопроекционного) преобразования для получения проекционно-исправленных изображений объектов. Описываемый метод нормализации изображений использует геометрические параметры (параллельность и ортогональность) и величину отношения неизвестных длин. С помощью приведенной методики можно точно воссоздать не только углы и относительную длину, но и абсолютное масштабирование