Ivan Detchev

Precise Photogrammetric Reconstruction Using Model-Based Image Fitting for 3D Beam Deformation Monitoring

Periodic structural health monitoring of infrastructure systems is important to avoid economic losses and human casualties. Traditionally, deformation monitoring has been done through surveying techniques. Recently, with the increased availability of inexpensive off-the-shelf cameras, photogrammetry has become a viable noncontact alternative for complete three-dimensional reconstruction of the object or surface of interest. This paper aims at combining two methodologies of photogrammetric reconstruction—image-matching-based reconstruction and model-based image fitting—to achieve submillimeter precision for the estimation of both vertical deflections and horizontal displacements. The proposed methodology was tested with data collected using a photogrammetric system at a structures laboratory where a concrete beam was subjected to different loading conditions by a hydraulic actuator. The experimental results showed that the photogrammetric system was capable of monitoring both static and dynamic deformations. The methodology used exhibited a high level of automation and the final results yielded a root-mean-square error (RMSE) of half a millimeter.

Stability Analysis for a Multi-Camera Photogrammetric System

Consumer-grade digital cameras suffer from geometrical instability that may cause problems when used in photogrammetric applications. This paper provides a comprehensive review of this issue of interior orientation parameter variation over time, it explains the common ways used for coping with the issue, and describes the existing methods for performing stability analysis for a single camera. The paper then points out the lack of coverage of stability analysis for multi-camera systems, suggests a modification of the collinearity model to be used for the calibration of an entire photogrammetric system, and proposes three methods for system stability analysis. The proposed methods explore the impact of the changes in interior orientation and relative orientation/mounting parameters on the reconstruction process. Rather than relying on ground truth in real datasets to check the system calibration stability, the proposed methods are simulation-based. Experiment results are shown, where a multi-camera photogrammetric system was calibrated three times, and stability analysis was performed on the system calibration parameters from the three sessions. The proposed simulation-based methods provided results that were compatible with a real-data based approach for evaluating the impact of changes in the system calibration parameters on the three-dimensional reconstruction.

Dynamic beam deformation measurements with off-the-shelf digital cameras

Abstract The physical health monitoring of civil infrastructure systems is an important task that must be performed frequently to ensure their serviceability and sustainability. Part of this process requires fine-scale monitoring of the structural elements that form the infrastructure system. This has traditionally been done with instrumentation, which either requires contact/access to the structural element, performs deformation measurements in only one dimension, or both. In order to avoid the downsides of the commonly used instrumentation systems, this paper proposes the use of a remote sensing approach based on a three dimensional photogrammetric system. The proposed system is low-cost, consists of off-the-shelf components, and is capable of targetless reconstruction. Also, based on the given configuration and the implemented processing techniques, the system yields deformation measurements well below the submillimetre level.

An Object-space Simulation Method for Low-cost Digital Camera Stability Testing

The widespread availability and low cost of digital cameras has been the impetus for their increased use for photogrammetric applications. The metric suitability of these cameras is critically dependent upon the stability of their interior orientation parameters (IOPs), which can be evaluated by simulation methods. Focused on aerial photogrammetry, this paper presents a new method that assesses the impact of camera stability in terms of the accuracy of object space terrain reconstruction from a large number of simulations. The results of this method are compared with those from two simulation procedures based on single-photo resection for ten sets of IOPs from three different low-cost digital cameras and are found to be in close agreement in terms of the decision about camera stability. Detailed analyses show the method is relatively insensitive to the distribution of ground control points used for camera orientation and the realism of the randomly-generated terrain, but is highly sensitive to the range of simulated terrain heights and image point measurement precision.

Evaluating the capability of time-of-flight cameras for accurately imaging a cyclically loaded beam

Time-of-flight cameras are used for diverse applications ranging from human-machine interfaces and gaming to robotics and earth topography. This paper aims at evaluating the capability of the Mesa Imaging SR4000 and the Microsoft Kinect 2.0 time-of-flight cameras for accurately imaging the top surface of a concrete beam subjected to fatigue loading in laboratory conditions. Whereas previous work has demonstrated the success of such sensors for measuring the response at point locations, the aim here is to measure the entire beam surface in support of the overall objective of evaluating the effectiveness of concrete beam reinforcement with steel fibre reinforced polymer sheets. After applying corrections for lens distortions to the data and differencing images over time to remove systematic errors due to internal scattering, the periodic deflections experienced by the beam have been estimated for the entire top surface of the beam and at witness plates attached. The results have been assessed by comparison with measurements from highly-accurate laser displacement transducers. This study concludes that both the Microsoft Kinect 2.0 and the Mesa Imaging SR4000s are capable of sensing a moving surface with sub-millimeter accuracy once the image distortions have been modeled and removed.

Practical In Situ Implementation of a Multicamera Multisystem Calibration

Consumer-grade cameras are generally low-cost and available off-the-shelf, so having multicamera photogrammetric systems for 3D reconstruction is both financially feasible and practical. Such systems can be deployed in many different types of applications: infrastructure health monitoring, cultural heritage documentation, bio-medicine, as-built surveys, and indoor or outdoor mobile mapping for example. A geometric system calibration is usually necessary before a data acquisition mission in order for the results to have optimal accuracy. A typical system calibration must address the estimation of both the interior and the exterior, or relative, orientation parameters for each camera in the system. This article reviews different ways of performing a calibration of a photogrammetric system consisting of multiple cameras. It then proposes a methodology for the simultaneous estimation of both the interior and the relative orientation parameters which can work in several different types of scenarios including a multicamera multisystem calibration. A rigorous in situ system calibration was successfully implemented and tested. The same algorithm is able to handle the equivalent to a traditional-style bundle adjustment, that is, a network solution without constraints, for a single or multicamera calibrations, and the proposed bundle adjustment with built-in relative orientation constraints for the calibration of a system or multiple systems of cameras.

ROBOT VISION: CALIBRATION OF WIDE-ANGLE LENS CAMERAS USING COLLINEARITY CONDITION AND K-NEAREST NEIGHBOUR REGRESSION

Abstract. Visual perception is regularly used by humans and robots for navigation. By either implicitly or explicitly mapping the environment, ego-motion can be determined and a path of actions can be planned. The process of mapping and navigation are delicately intertwined; therefore, improving one can often lead to an improvement of the other. Both processes are sensitive to the interior orientation parameters of the camera system and mathematically modelling these systematic errors can often improve the precision and accuracy of the overall solution. This paper presents an automatic camera calibration method suitable for any lens, without having prior knowledge about the sensor. Statistical inference is performed to map the environment and localize the camera simultaneously. K-nearest neighbour regression is used to model the geometric distortions of the images. A normal-angle lens Nikon camera and wide-angle lens GoPro camera were calibrated using the proposed method, as well as the conventional bundle adjustment with self-calibration method (for comparison). Results showed that the mapping error was reduced from an average of 14.9 mm to 1.2 mm (i.e. a 92 % improvement) and 66.6 mm to 1.5 mm (i.e. a 98 % improvement) using the proposed method for the Nikon and GoPro cameras, respectively. In contrast, the conventional approach achieved an average 3D error of 0.9 mm (i.e. 94 % improvement) and 6 mm (i.e. 91 % improvement) for the Nikon and GoPro cameras, respectively. Thus, the proposed method performs more consistently, irrespective of the lens/sensor used: it yields results that are comparable to the conventional approach for normal-angle lens cameras, and it has the additional benefit of improving calibration results for wide-angle lens cameras.