Browse Topic: Photogrammetry
Testing aircraft antennas is challenging since optimal tests are made after antenna installation. Aircraft are often taken to anechoic antenna test facilities which create long lead times, transportation hassle, and very high costs. Portable alternatives exist but often have compromised testing fidelity. Innovators at the NASA Glenn Research Center have developed the PLGRM system, which allows an installed antenna to be characterized in an aircraft hangar. All PLGRM components can be packed onto pallets, shipped, and easily operated.
The 3D crush model can be obtained by any suitable photogrammetry method using this image set and is intended to graphically represent in photographs the shape and orientation of the damaged surface(s) relative to the undamaged, or least damaged, portion of the vehicle. The procedure is intended to provide an image set sufficient to determine, with the use of photogrammetric methodologies, the 3D location of points on the crushed surface of the damaged vehicle. Measurement of the exterior damaged surface(s) on a vehicle is a necessary step in quantifying the deformation caused by a collision and the energy dissipated by the deformation process. The energy analysis is sometimes called a crush analysis. Evaluation of the energy dissipated is useful in reconstructing the change in the velocity of the vehicles (delta-V) involved in a collision. This guideline is intended for use by investigators who do not have photogrammetry expertise, special equipment or training and may be constrained to a short time period for photography. The person who captures the image set is not expected to perform the photogrammetry or to necessarily understand how it is accomplished. It may be used by law enforcement personnel, safety officials, insurance adjusters, and other interested parties. This guideline has a written body which explains the foundation for the procedure and an appendix which specifies the procedure with accompanying diagrams. Appendix A is intended to be used as a separable field guide. This procedure recommends capturing more images than are absolutely necessary rather than specifying the criteria for selecting the absolute minimum set.
A new spatial calibration procedure has been introduced for infrared optical systems developed for cases where camera systems are required to be focused at distances beyond 100 meters. Army Combat Capabilities Development Command Armaments Center, Picatinny Arsenal, NJ All commercially available camera systems have lenses (and internal geometries) that cannot perfectly refract light waves and refocus them onto a two-dimensional (2D) image sensor. This means that all digital images contain elements of distortion and thus are not a true representation of the real world. Expensive high-fidelity lenses may have little measurable distortion, but if sufficient distortion is present, it will adversely affect photogrammetric measurements made from the images produced by these systems. This is true regardless of the type of camera system, whether it be a daylight camera, infrared (IR) camera, or camera sensitive to another part of the electromagnetic spectrum. The most common examples of large-scale lens distortions are known as barrel and pincushion effects, which are illustrated in figure 1. If these images were a truly planar reproduction of the real world, the curved lines in the images would appear as straight lines. Essentially, this can be thought of as the focal length (conversion from pixel distance to real-world distance) not being uniform throughout the image. Spatial calibration aims to build a transform to correct for large-scale distortion effects and effectively flatten an image so that the focal lengths (x and y) are uniform throughout the field of view.
All commercially available camera systems have lenses (and internal geometries) that cannot perfectly refract light waves and refocus them onto a two-dimensional (2D) image sensor. This means that all digital images contain elements of distortion and thus are not a true representation of the real world. Expensive high-fidelity lenses may have little measurable distortion, but if sufficient distortion is present, it will adversely affect photogrammetric measurements made from the images produced by these systems. This is true regardless of the type of camera system, whether it be a daylight camera, infrared (IR) camera, or camera sensitive to another part of the electromagnetic spectrum.
NASA researchers have developed a compact, cost-effective imaging system using a co-linear, high-intensity LED illumination unit to minimize window reflections for background-oriented schlieren (BOS) and machine vision measurements. The imaging system tested in NASA wind tunnels can reduce or eliminate shadows that occur when using many existing BOS and photogrammetric measurement systems; these shadows occur in existing systems for a variety of reasons, including the severe back-reflections from wind tunnel viewing port windows and variations in the refractive index of the imaged volume.
The paper focuses on the analyses of photogrammetry measurement results on the two-blade, hingeless MERIT rotor with diameter 1.8 m at the two rotor speeds 900 and 1800 RPM and collective pitch angles 0-12 in hover and their comparison to simulation results calculated in CAMRAD II. Blade tip displacements in flap, lead-lag, axial, and torsional direction are shown as a function of collective pitch and rotor speed. Radial displacements in flap and lag direction depict the influence of the pitch bearing play and blade attachment. The structural blade model is validated by using static DIC deformation measurements and shows very good agreement. Calculated and measured thrust polars match very well with the use of a free wake model in the simulation. The combination of measured flapping angle and calculated flapping moment gives a stiffness estimation for a virtual flap hinge. The influence of hinge offset and stiffness are shown by parameter adjustment. Flap deformations of the rotating blades leave a tip flap offset of less than 1 mm in average, which corresponds to the statically measured effect of the bearing play at the blade tip. The study shows that photogrammetry is a valuable tool to identify and tune parameters of a numerical model.
The world is going through the fourth industrial revolution, where digital transformation is one of the global market trends. To maintain competitive advantages and sustainable businesses, an increasing number of companies and organizations are embracing digital transformation processes. These organizations are changing their business and processes and creating new business models with the help of digital technologies. Taking all industries and business models to unprecedented heights and in a certain way consolidating globalization. For such digital transformation, technologies like IoT (Internet of Things), artificial intelligence, machine learning, neural networks, and others are increasingly common. This paper seeks to define what technical aspects are involved to implement digitalization in the process of vehicle collision data analysis. In this sense, insurance companies are aware of the changes and are trying to follow the trends and updating themselves to provide better services and with better quality. The applied methodology is divided into an analysis of the existing 3D metrology techniques and a pilot project in order to evaluate the application of the selected technique and analyze results and gains in the incident inspection process. The set of these methodologies allowed the proposed application to be validated.
Traffic cameras, dash-cameras, surveillance cameras, and other video sources increasingly capture critical evidence used in the accident reconstruction process. The iNPUT-ACE Camera Match Overlay tool can utilize photogrammetry to project a two-dimensional video onto three-dimensional point cloud software to enable measurements to be directly taken from the video. Those measurements are commonly used, and critical for, the determination of vehicle speed in accident reconstruction. The accuracy of the Camera Match Overlay tool has not yet been thoroughly examined. To validate the use of the tool to measure vehicle speed for accident reconstruction, data were collected from a series of tests involving three traffic cameras, a stationary and moving dash-camera, a stationary and moving cell-phone camera, and a doorbell surveillance camera. Each camera provided unique specifications of quality and focal length to ensure the tool would be tested in a variety of scenarios. Vehicles drove past the various cameras at a variety of distances and angles with vehicle speeds that ranged from approximately 15.5 kph (9.6 mph) to 90.2 kph (56.1 mph) as recorded by a RACELOGIC VBOX GPS system. A bright flash was utilized to synchronize timing between VBOX and video data. The resulting comparison between VBOX data and the tool’s distance and timing data revealed the iNPUT-ACE Camera Match Overlay tool was an effective method for analyzing vehicle speed across a variety of video sources.
This paper introduces a method for calculating vehicle speed and uncertainty range in speed from video footage. The method considers uncertainty in two areas; the uncertainty in locating the vehicle’s position and the uncertainty in time interval between them. An abacus style timing light was built to determine the frame time and uncertainty of time between frames of three different cameras. The first camera had a constant frame rate, the second camera had minor frame rate variability and the third had more significant frame rate variability. Video of an instrumented vehicle traveling at different, but known, speeds was recorded by all three cameras. Photogrammetry was conducted to determine a best fit for the vehicle positions. Deviation from that best fit position that still produced an acceptable range was also explored. Video metadata reported by iNPUT-ACE and Mediainfo was incorporated into the study. When photogrammetry was used to determine a vehicle’s position and speed from video recorded by a constant frame rate camera, the results closely matched the speeds reported by the instrumented vehicle being measured. This low uncertainty resulted from the constant frame rate eliminating error in time, and from low error in the vehicle’s position through photogrammetry. For the variable frame rate camera, uncertainty in speed was dependent on the time between frames analyzed as well as any uncertainty in position. Quantification of this uncertainty has value for the reconstructionist. Determining speed of the vehicle in the variable frame rate video could be improved by incorporating frame timing reported by iNPUT-ACE or through other video analysis techniques and software that measure precise time differences between each frame.
Forensic disciplines are called upon to locate evidence from a single camera or static video camera, and both the angle of incidence and resolution can limit the accuracy of single image photogrammetry. This research compares a baseline of known 3D data points representing evidence locations to evidence locations determined through single image photogrammetry and evaluates the effect that object resolution (measured in pixels), and angle of incidence has on accuracy. Solutions achieved using an automated process where a camera match alignment is calculated from common points in the 2D imagery and the 3D environment, were compared to solutions achieved in a more manual method by iteratively adjusting the camera’s position, orientation, and field-of-view until an alignment is achieved. This research independently utilizes both methods to achieve photogrammetry solutions and to locate objects within a 3D environment. Results are compared for a greater understanding of the accuracies that can be achieved using camera matching photogrammetry for evidence placement when only a single image is available.
Photogrammetry is a commonly used and accepted technique within the field of accident reconstruction for taking measurements from photographs. Previous work has shown the accuracy of optimized close-range photogrammetry techniques to be within 2 mm compared to other high accuracy measurement techniques when using a known calibrated camera. This research focuses on the use of inverse camera close-range photogrammetry, where photographs from an unknown camera are used to model a vehicle. Photogrammetry is a measurement technique that utilizes triangulation to take measurements from photographs. The measurements are dependent on the geometry of the camera, such as the sensor size, focal length, lens type, etc. Three types of cameras were tested for accuracy; a high-end commercial camera, a point and shoot camera, and a cell phone camera. This study indicates that in a properly conducted inverse photogrammetry project, an analyst can be 95% confident the true position of a point will be within 6.25 mm (0.25 inches) of the location of the point obtained via photogrammetry.
A new measurement capability was created by combining photogrammetry and metrology techniques to accurately measure one half of the XV-15 Tilt Rotor Research Aircraft at the Smithsonian’s Udvar-Hazy museum. The challenges imposed by the fuselage and surrounding environment at Udvar-Hazy were overcome by careful application of photogrammetry and metrology techniques. Data analyses and processing included the use of multiple reverse engineering programs to accurately generate a complete 3-dimensional water-tight geometry of the aircraft and rotor blade. This paper describes the photogrammetry and metrology measurement systems, technology and hardware set-up, data analysis and processing methods, future work, and lessons learned. In addition, selected measurement results of the fuselage and rotor blade are presented.
Feasibility in Manufacturing of autonomous unmanned aerial vehicles at low cost allows the UAV developers to bring it out with numerous applications for society. Civil domain is a widely developing platform which initiated the development of UAV for civilian applications like bridge inspection, building monitoring, life or strength estimation of historical places and also outdoor and indoor mapping of buildings. These autonomous UAVs with high resolution camera fly over and around the construction sites, buildings, mines and captures images of various locations and point clouds in all sides of the building and creates a 3D map by using photogrammetry techniques. The software auto generates the report and updates it to the cloud which can be accessed online. Autonomous operations are quite difficult in new environments which requires SLAM (simultaneous localization and mapping) to operate the UAV between open spaces. This paper describes the technique of mapping a construction site using a quadcopter and determine the completion of such constructions using image processing and machine learning techniques. Obstacle avoidance during the autonomous flight using ultrasonic sensors provide greater flexibility of the vehicle to move around the buildings.
In this paper will be explained how photogrammetry and tracking technologies are a highly accurate alternative to accelerometers instrumented sensors related to distances calculations between objects or vehicle interior parts and the dummies. Photogrammetry is used to calculate the real-world point’s position on an image. The tracking system uses algorithms to follow points and keep the same center point at each movie frame. A software application combines these two elements to provide position, velocity, acceleration and angles of every point on the movie for the 3-dimensional axis. The tracking technology can be applied for on dummy’s analysis head impact criterion (HIC) against internal structure and objects as the pole. The use of internal sensors for this kind of analysis, only offers a yes/no response and yet tracking provides the exact distance between head and the interior components. Using tracking technology the distance between the dummy’s head and any other structural part of the vehicle can be known and even the distance between the head and the steering wheel can be calculated. Therefore, knowing the distances and the resultant forces from the sensors, improvements at the restraint systems can be done in order to decrease the severity implemented to the dummy from the restraint systems itself. In this paper several crash test had been analyzed to determine if the implemented methodology is accurate. Results are shown and conclusions about the benefits of using the tracking technology are explained.
Accident reconstructionists will typically document scenes, evidence, vehicles or objects of interest by using 3-dimensional laser scanners. These techniques are well documented, utilized and can be extremely accurate. However, when the subject of documentation involves surfaces that include intricate, highly reflective, and/or complex geometry (motorcycles, wheelchairs, stairs, etc.) the commercially available laser scanners can produce obscuring dense stray and scattered points which results in point clouds that could require tedious manual registration and/or optimization. This paper compares a FARO Focus laser scanner, Pix4DMapper, and Agisoft’s Photoscan point cloud data to FARO ARM measurements of vehicles, other transportation devices and architectural features. It was shown that the Pix4DMapper and Agisoft’s Photoscan point cloud data resulted in detailed and accurate point cloud data compared to the FARO ARM measurements. Additionally, the input data for Pix4DMapper and Agisoft’s Photoscan is easy to capture and required minimal processing and did not require extensive, time consuming, optimization of individual scans. This paper demonstrates the use of contemporary photogrammetry softwares, Pix4DMapper and Agisoft’s Photoscan, as accurate, time and cost effective alternatives to laser scanners.
In an accident reconstruction, vehicle speeds and positions are always of interest. When provided with scene photographs or fixed-location video surveillance footage of the crash itself, close-range photogrammetry methods can be useful in locating physical evidence and determining vehicle speeds and locations. Available 3D modeling software can be used to virtually match photographs or fixed-location video surveillance footage. Dash- or vehicle-mounted camera systems are increasingly being used in light vehicles, commercial vehicles and locomotives. Suppose video footage from a dash camera mounted to one of the vehicles involved in the accident is provided for an accident reconstruction but EDR data is unavailable for either of the vehicles involved. The literature to date describes using still photos to locate fixed objects, using video taken from stationary camera locations to determine the speed of moving objects or using video taken from a moving vehicle to locate fixed objects. However, techniques to evaluate the position, speed and acceleration of moving objects seen in video taken from moving locations have not been evaluated. To address the increasing prevalence of dash cams and other in-vehicle video and the value in using such video in vehicle crash reconstruction, this paper describes techniques for determining the position and speed of a moving object from digital video taken from a moving vehicle. Evaluations of the accuracy of those techniques were done when provided three different levels of information about the environment: 1 Aerial Photography (USGS) 2 Survey Data (Total Station) 3 3D Scan Data (of both the environment and vehicles)
Photogrammetry is widely used in the automotive and accident reconstruction communities to extract three-dimensional information from photographs. Prior studies in the literature have demonstrated the accuracy of such methods when photographs contain easily-identifiable, distinct points; however, it is often desirable to determine measurements for locations where a seam, edge, or contour line is available. To exploit such details, an analyst can control the direction that the epipolar line is projected onto the camera plane by strategic selection of photographs. This process constrains the search for the corresponding 3D point to a straight line that can be projected perpendicular to the seam, edge, or contour line. Thus, the goal of this study was to evaluate the modeling accuracy for cases in which an analyst uses epipolar lines in a workflow. To do so, artificial images were created using a computer-generated camera within a computer-assisted drawing environment to allow for a known reference model to compare with results produced using photogrammetry. A systematic study was undertaken by modeling two-dimensional curves on a plane, three-dimensional curves on a curved surface, and then curved edges on a vehicle model. Each model was assessed for accuracy, and the sensitivity of the accuracy to camera placement was carefully examined and explained. Finally, the procedures were applied to an actual vehicle, for which the results were compared to a 3D laser scan of the vehicle. In conclusion, the average residual error between a photogrammetry model created with the aid of epipolar lines and 3D scanned points for a three-dimensional vehicle edge feature was 1.69 mm (SD = 0.55 mm).
Improvements in computer image processing and identification capability have led to programs that can rapidly perform calculations and model the three-dimensional spatial characteristics of objects simply from photographs or video frames. This process, known as structure-from-motion or image based scanning, is a photogrammetric technique that analyzes features of photographs or video frames from multiple angles to create dense surface models or point clouds. Concurrently, unmanned aircraft systems have gained widespread popularity due to their reliability, low-cost, and relative ease of use. These aircraft systems allow for the capture of video or still photographic footage of subjects from unique perspectives. This paper explores the efficacy of using a point cloud created from unmanned aerial vehicle video footage with traditional single-image photogrammetry methods to recreate physical evidence at a crash scene. The unique aspects of photographs or video taken with unmanned aircraft systems ease some of the challenges of creating point cloud data with ground level footage. To explore the accuracy of this process, the authors constructed a mock scene with physical evidence that is typical of vehicular crashes. The scene was scanned with a FARO laser scanner and photographed. The evidence was then removed and video was taken of the scene from an unmanned aerial vehicle. That video footage was processed with image-based scanning software to create a point cloud, and the point cloud was used as a means to determine the positions and characteristics of the camera at the time the evidence was photographed. The evidence was then reconstructed with traditional single image photogrammetry techniques, and the position and size of the reconstructed evidence was compared to the actual position as documented by the FARO scanner. Through this process, the authors determined that the use of unmanned aerial vehicle footage and image-based scanning software could be used to accurately reconstruct the location of physical evidence.
This paper presents a methodology for determining the position and speed of objects such as vehicles, pedestrians, or cyclists that are visible in video footage captured with only one camera. Objects are tracked in the video footage based on the change in pixels that represent the object moving. Commercially available programs such as PFTracktm and Adobe After Effectstm contain automated pixel tracking features that record the position of the pixel, over time, two dimensionally using the video’s resolution as a Cartesian coordinate system. The coordinate data of the pixel over time can then be transformed to three dimensional data by ray tracing the pixel coordinates onto three dimensional geometry of the same scene that is visible in the video footage background. This paper explains the automated process of first tracking pixels in the video footage, and then remapping the 2D coordinates onto three dimensional geometry using previously published projection mapping and photogrammetry techniques. The results of this process are then compared to VBOX recordings of the objects seen in the video to evaluate the accuracy of the method. Some beneficial aspects of this process include the time reduced in tracking the object, since it is automated, and also that the shape and size of the object being tracked does not need to be known since it is a pixel being tracked, rather than the geometry of the object itself.
Video and photo based photogrammetry software has many applications in the accident reconstruction community including documentation of vehicles and scene evidence. Photogrammetry software has developed in its ease of use, cost, and effectiveness in determining three dimensional data points from two dimensional photographs. Contemporary photogrammetry software packages offer an automated solution capable of generating dense point clouds with millions of 3D data points from multiple images. While alternative modern documentation methods exist, including LiDAR technologies such as 3D scanning, which provide the ability to collect millions of highly accurate points in just a few minutes, the appeal of automated photogrammetry software as a tool for collecting dimensional data is the minimal equipment, equipment costs and ease of use. This paper evaluates the accuracy and capabilities of four automated photogrammetry based software programs to accurately create 3D point clouds, by comparing the results to 3D scanning. Both a damaged and undamaged vehicle were documented with video and photographs and on average the damaged vehicle set returned more data points with higher accuracy than the undamaged vehicle set. Four cameras types were evaluated and more accurate results were achieved when using either a DSLR or a point-and-shoot camera than when using a GoPro, or a cell phone camera. Photogrammetry data from video footage was analyzed and found to be both less accurate and to return less data than photographs. By limiting the number of photographs used, it was found that a photogrammetry solution could be achieved with as few as 16 photographs encircling a vehicle, but better results were reached with a larger number of photographs.
Two Transport Rotorcraft Airframe Crash Testbed (TRACT) full-scale tests were performed at NASA Langley Research Center's Landing and Impact Research Facility in 2013 and 2014. Two CH-46E airframes were impacted at 33-ft/s forward and 25-ft/s vertical combined velocities onto soft soil, which represents a severe, but potentially survivable impact scenario. TRACT 1 provided a baseline set of responses, while TRACT 2 included retrofits with composite subfloors and other crash system improvements based on TRACT 1. For TRACT 2, a total of 18 unique experiments were conducted to evaluate ATD responses, seat and restraint performance, cargo restraint effectiveness, patient litter behavior, and activation of emergency locator transmitters and crash sensors. Combinations of Hybrid II, Hybrid III, and ES-2 Anthropomorphic Test Devices (ATDs) were placed in forward and side facing seats and occupant results were compared against injury criteria. The structural response of the airframe was assessed based on accelerometers located throughout the airframe and using three-dimensional photogrammetric techniques. Analysis of the photogrammetric data indicated regions of maximum deflection and permanent deformation. The response of TRACT 2 was noticeably different in the longitudinal direction due to changes in the cabin configuration and soil surface, with higher acceleration and damage occurring in the cabin. Loads from ATDs in energy absorbing seats and restraints were within injury limits. Severe injury was likely for ATDs in forward facing passenger seats.
In the field of accident reconstruction, a reconstructionist will often inspect a crash scene months or years after a crash has occurred. With this passage of time important evidence is sometimes no longer present at the scene (i.e. the vehicles involved in the crash, debris on the roadway, tire marks, gouges, paint marks, etc.). When a scene has not been totally documented with a survey by MAIT or the investigating officers, the reconstructionist may need to rely on police, fire department, security camera, or witness photographs. These photos can be used to locate missing evidence by employing traditional photogrammetric techniques. However, traditional techniques require planar surfaces, matched discrete points, or camera matching at the scene. Sometimes it is not possible to survey discrete points or perform camera matching at the scene due to lack of access (the tops of power poles, elevated bridge features, or objects at a great distance) or for safety reasons (interstate highways with high traffic conditions or on narrow bridges). Other times important evidence can be located on a hill or depressed median and planar photogrammetric methods are not effective. In recent years three dimensional laser scanners have been utilized by accident investigators allowing the reconstructionist to document a crash scene with millions of points in minutes so that a “point cloud” can be created. The equipment is fast, simple to setup, and is very accurate, thus allowing the reconstructionist the ability to take home a three dimensional model of the scene for in depth analysis. This paper will describe the scanning equipment, photo considerations, and a methodology for utilizing three dimensional laser scan data and camera matching to extract evidence from first responder photographs or videos. The accuracies of this technique are compared to other accepted methods such as planar photogrammetry, Discrete Point Software, and photo rectification over point cloud data. The camera matching method of locating evidence has been described in literature before [1,2,7] but technology advances have allowed the method to be expanded upon. The camera matching method now applied to a three dimensional point cloud with millions of points used for the solution allows easy and accurate extraction of evidence on not only flat areas but complex terrains as well. A simulated crash which is called a “staged collision” is analyzed. This “staged collision” represents two types of evidence. One type of evidence is on a flat surface to compare planar accuracies. The second type of evidence is in an area incorporating large changes in the terrain to compare three dimensional accuracies. Various photogrammetry methods are compared in these two types of terrain and it is shown how combining laser scan data with the “virtual camera matching to point cloud” technique can accurately extract evidence from non-planar areas with large changes in terrain.
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