Interior Orientation Research Articles

3D INDOOR CORRIDOR MAPPING WITH CALIBRATED ROPS OF A MULTI-FISHEYE CAMERA-RIG

Abstract. We present a 3D indoor mapping simulation in the location with limitations to access (i.e., pipeline drainage, sewer, box girder, etc.) by utilising a multi-fisheye camera rig. This camera rig (i.e., Pilot One) offers a 360 Field of View (FoV) coverage that represents an efficient data acquisition solution. Particular attention is focused on camera calibration consists of Interior Orientation Parameters (IOPs) and Relative Orientation Parameters (ROPs) to generate an accurate 3D reconstruction result. An appropriate Self-Calibration Bundle Adjustment (SCBA) approach, assisted with Australis coded targets, is conducted to have stable pre-calibration parameters that can be applied in a 68 meters corridor study field. We performed forward and backward data acquisition with unstitched original images and stitched images separately in a straight line to simulate data acquisition for future applications. However, due to the camera model and uncalibrated images, there is a “banana effect” in the stitched image. Twenty scale bars are used in this study. To achieve the ideal position and orientation of the scale bars, we attempt to construct a number of scenarios. The objective of this simulation is to determine the minimum number of scale bars that can be applied to a corridor where is difficult to access. We try to set up without any Control Scale Bars (CSB) with 20 Check Scale Bars (CkSBs). Even though this trial only has a 3.8 cm CkSBs error, it produced a length estimation error of 2.3 m (i.e., 3.37% of 68.1m) when compared with Terrestrial Laser Scanning.

Historical Structure from Motion (HSfM): Automated processing of historical aerial photographs for long-term topographic change analysis

Precisely measuring the Earth’s changing surface on decadal to centennial time scales is critical for many science and engineering applications, yet long-term records of quantitative landscape change are often temporally and geographically sparse. Archives of scanned historical aerial photographs provide an opportunity to augment these records with accurate elevation measurements that capture the historical state of the Earth surface. Structure from Motion (SfM) photogrammetry workflows produce high-quality digital elevation models (DEMs) and orthoimage mosaics from these historical images, but time-intensive tasks like manual image preprocessing (e.g., fiducial marker identification) and ground control point (GCP) selection impede processing at scale. We developed an automated method to process historical images and generate self-consistent time series of high-resolution (0.5–2 m) DEMs and orthomosaics, without manual GCP selection. The method relies on SfM to correct camera interior and exterior orientation and a robust multi-stage co-registration approach using modern reference terrain datasets for geolocation refinement. We demonstrate the method using scanned images from the North American Glacier Aerial Photography (NAGAP) archive collected between 1967 and 1997. We present results for two sites with variable photo acquisition geometry and overlap — Mount Baker and South Cascade Glacier in Washington State, USA. The automated method corrects initial camera position errors of several kilometers and produces accurately georeferenced, high-resolution DEMs and orthoimages, regardless of camera configuration, acquisition geometry, terrain characteristics, and reference DEM properties. The average RMS reprojection error following bundle adjustment optimization was 0.67 px (0.15 m) for the 261 images contributing to 10 final DEM mosaics between 1970 and 1992 at Mount Baker, and 0.65 px (0.13 m) for the 243 images contributing to 18 individual DEMs between 1967 and 1997 at South Cascade Glacier. The relative accuracy of elevation values in the historical time series stacks was 0.68 m at Mount Baker and 0.37 m at South Cascade Glacier. Our products have reduced systematic error and improved accuracy compared to DEM products generated using SfM with manual GCP selection. Final elevation change measurement precision was ∼0.7–1.0 m over a 30-year period, enabling the study of processes with rates as low as ∼1-3 cm/yr. Our results demonstrate the potential of this scalable method to rapidly process archives of historical imagery and deliver new quantitative insights on long-term geodetic change and Earth surface processes.

How does the architecture in the movie Parasite help portray the social class differences?

Social class difference is the central plot in the film “Parasite”. The movie shows the difference in lifestyles of the upper-class and lower-class. One way it does so is through the architecture portrayed in it, which is precisely what this research paper focuses upon. The subthemes of architecture chosen for this paper include spatial elevation, interior orientation, and perspective of the outside world from the upper-class and lower-class streams. Different points are characterized into these three groups and are explored as a whole in the film. This research paper also tries to identify why a certain setting has been used, why has the layout been made a certain way, and why has a certain object been placed in a particular spot. All the answers to these questions mainly point in the direction of creating an empathetic response from the audience. This refers to the audience feeling sorry for the low-income Kim household and hence actively depicting their plight. As the film is set in South Korea, it can be derived that it not only highlights the general wage gap but rather specifically the one in Korea as it is filled with poverty and unequally distributed income. Moreover, architecture itself might have been one chosen medium to bring the social difference to notice as it is very subtle in nature. It makes the audience sympathize without them even realizing when or why. 

Geo3o1: a tool for 3D georeferencing accuracy assessment of tri-stereo images by state-of-the-art sensor dependent orientation model

We subjected a tool for 3D georeferencing accuracy assessment of tri-stereo primary images of Pléiades 1A/1B/Neo, SPOT 6/7, and Göktürk-1, developed in the MATLAB environment considering their private sensor-dependent orientation model. Although the orientation model has been developed by the image vendors, the major contribution of Geo3o1 was the two-step adjustment process. The basic stages of estimating the 3D georeferencing accuracy were (i) estimating the direct georeferencing accuracy by raw orientation parameters, (ii) pre-adjustment of look angles using ground control points (GCPs), and (iii) bundle adjustment. In addition, the efficiency and validation of exterior orientation parameters and also the correlation among interior and exterior orientation parameters could be analyzed. Geo3o1 has the capability of handling the images in stereo or tri-stereo modes. The case study has been carried out by the Pléiades 1A panchromatic tri-stereo primary images with 50-cm geometric resolution. About 90 points were used as GCPs/ICPs in two distribution scenarios. The accuracy on ground was estimated centimeter level at GCPs whereas the accuracy was naturally coarser at ICPs at ∼ ± 1 GSD. The efficiency and validation of exterior orientation parameters and the correlations were also shown by two samples. The research was carried out over an undulating, mountainous area in Zonguldak, Türkiye.

Automatic orientation of historical terrestrial images in mountainous terrain using the visible horizon

Historical terrestrial images are the only visual sources documenting alpine environments shortly after the end of the Little Ice Age. Despite their unique value, they are largely unused for quantifying environmental changes because of the difficult and time-consuming estimation of the unknown camera parameters. For most images large parts of the captured scenery have vastly changed over time, making automatic feature point matching infeasible. In contrast, the visible image horizon seems to remain stable over time and hence, appears to be a suitable feature for image orientation. Since the focal length is unknown for historical terrestrial images, existing methods, focusing solely on estimating the exterior orientation of recent imagery, can not be applied. Accordingly, it was investigated if the horizon is suitable to estimate both the interior and exterior orientation of historical terrestrial images, with an accuracy comparable to manually oriented images. In a first step, the whole horizon was used to approximate the unknown camera parameters, reducing the potential search space. In the subsequent spatial resection these approximations were further refined using salient points along the horizon. We evaluated our approach using 204 manually oriented reference images. With the proposed method the accuracy of the estimated exterior orientation could be significantly improved compared to previous works. Additionally, the unknown focal length was estimated within 5% of the true focal length for 75% of the images. As historical terrestrial images are commonly used for monoplotting, the accuracy for 2400 manually selected checkpoints was evaluated. This analysis showed that for 63% of the images the same accuracy as with manually oriented images was achieved. For additional 22% the estimated camera parameters were still accurate enough to serve as initial estimates for a subsequent manual orientation. In 15% of the images our method completely failed. Due to the vastly changing scenery and oblique viewing geometry, finding the initial camera parameters, in our experience, is often the most challenging and time consuming step during manual orientation of historical images. Hence, in 85% of the images this initial step can be replaced with our method, leading to a significantly reduced effort for orienting whole collections of historical terrestrial images.

Accuracy Assessment of Establishing 3D Real Scale Model in Close-Range Photogrammetry with Digital Camera

Three-dimensional (3D) real scale models delivered from digital photogrammetric techniques have rapidly increased to meet the requirements of many applications in different fields of daily life. This paper deals with the establishment of a 3D real scale model from a block of images (18 images) that were captured by using Canon EOS 500D digital camera to cover a test field area consisting of 90 artificial target points, 25 of them are ground control points (GCPs) while the remains are checkpoints (CPs). The analytical photogrammetric processes including the calculation of interior orientation parameters (IOPs) of the camera during the camera calibration process, exterior orientation parameters (EOPs) of the camera in each capturing, and the object space (ground) coordinates of the model are calculated simultaneously based on collinearity equation using bundle block adjustment method (BBA). Assessment and validation of the accuracy of the results is an important task in this study that was implemented to determine and analyze the errors of 3D coordinates through linear regression analysis (LRA). Root mean square error (RMSE) is the statistical parameter that was used in the statistical analysis of results. The standard error is another statistical parameter which also used to evaluate the accuracy of locations and rotation angles (EOPs) of cameras. The total RMSE (RMSE)xyz of GCPs is ± 2.530 mm while the total RMSE (RMSExyz) of CPs is ± 2.740 mm. The overall accuracy of the work is 5.000 mm.

Simulated Photogrammetric Data for Testing the Performance of Photogrammetric Instruments and Systems

The generation of mathematical photograms (photos) as data is universally accepted as the basis for photogrammetric studies. New development in the field of computer technology has had a major influence on photogrammetry. This paper aims to describe the development of a computer system called SimuPhotos designed to produce simulated data in different forms for testing photogrammetric methodologies, software, and instruments. SimuPhotos is capable of generating photogrammetric blocks with different specifications. It generates the simulated data for the whole block, strips, or selected photos. The simulated free error data include the camera interior orientation parameters, ground coordinates of points, and for each photograph, camera exterior orientation parameters, and the photo coordinates of imaged points. To increase the capabilities of the developed system, it has a special error generator to get normally distributed error(s) with arbitrary mean(s) and standard deviation(s). The obtained errors are then, if required, applied to the error-free photo and ground coordinates of points. The developed system interfaces CAD technology to generate simulated photogrammetric data in DXF and raster formats which are suitable for testing the photogrammetric instruments and softcopy systems. SimuPhotos has been used for testing the analytical photogrammetric system PhotoMap. The results showed that PhotoMap is free of system error and is functional. Also, the results showed that SimuPhotos can effectively provide a convenient, economical, and accurate testing tool for photogrammetric systems. The developed system is quite versatile and affordable.

Orthorectification of Fisheye Image under Equidistant Projection Model

The fisheye camera, with its large viewing angle, can acquire more spatial information in one shot and is widely used in many fields. However, a fisheye image contains large distortion, resulting in that many scholars have investigated its accuracy of orthorectification, i.e., generation of digital orthophoto map (DOM). This paper presents an orthorectification method, which first determines the transformation relationship between the fisheye image points and the perspective projection points according to the equidistant projection model, i.e., determines the spherical distortion of the fisheye image; then introduces the transformation relationship and the fisheye camera distortion model into the collinearity equation to derive the fisheye image orthorectification model. To verify the proposed method, high accuracy of the fisheye camera 3D calibration field is established to obtain the interior and exterior orientation parameters (IOPs/EOPs) and distortion parameters of the fisheye lens. Three experiments are used to verify the proposed orthorectification method. The root mean square errors (RMSEs) of the three DOMs are averagely 0.003 m, 0.29 m, and 0.61 m, respectively. The experimental results demonstrate that the proposed method is correct and effective.

A high-efficiency and high-precision automatic 3D scanning system for industrial parts based on a scanning path planning algorithm

To achieve high-precision and high-efficiency 3D scanning of industrial parts, this research proposes an automated 3D scanning system based on path planning algorithms, in which the 3D scanning sensor is realized based on the principle of raster projection. The proposed automated scanning system is composed of a robotic arm, a 3D scanner, and a turntable to achieve a complete 3D scanning of the object to be measured. More precisely, this paper proposes a virtual 3D scanning principle, which can accurately determine whether a certain point on the CAD model can be scanned by a viewpoint. Further, this paper proposes a path planning algorithm based on the virtual 3D scanning principle, which can calculate the optimal scanning path required to scan the object according to the CAD model of the scanned object. Furthermore, to ensure the accuracy of the system, a high-precision system calibration method is proposed to establish the accurate positional relationship between various parts of the system. Firstly, this research proposes a camera calibration algorithm based on close-range photogrammetry, which can calculate the high-precision interior and exterior orientation parameters of the camera, and apply the method of calculating camera external parameters to the hand-eye calibration, making the accuracy of hand-eye calibration is also improved accordingly. In addition, this research proposes a photogrammetry-based projector calibration algorithm to determine the positional relationship between the projector and the camera in the 3D scanner, and a turntable calibration method to determine the positional relationship between the rotation center of the turntable and the camera. Eventually, according to different usage requirements, two 3D scanning data splicing methods are proposed. One is a high-efficient automatic splicing method based on the results of system calibration without pasting mark points, and the other is a high-precision data splicing method based on photogrammetry. Experimental results demonstrate that this system has higher accuracy and better performance in system calibration, scanning path calculation, and multi-view scanning data splicing.

CALIBRATION OF PHOTOGRAMMETRIC SYSTEM FOR 3D MEASUREMENTS IN INACCESSIBLE MULTIMEDIA WORKING SPACE

Abstract. Hydrodynamic tunnel has proved to be an effective mean for studying aerodynamic processes using scaled models of real aircrafts. The specificity of study in a hydrodynamic tunnel requires to apply non-contact measuring techniques, such as photogrammetry, for retrieving information on geometrical characteristics of the flow. So reliable calibration of photogrammetric system is a key element for trustworthy analysis of flow behaviour. For 3D measurements in a hydrodynamic tunnel calibration technique must account for refraction effects at optical media interfaces. But often the design of a hydrodynamic tunnel does not allow to perform standard calibration procedure based on image acquisition of a special test field, placed in the working space of the measuring system. The presented study addresses this problem – developing the technique for accurate photogrammetric calibration, for the case of the working space being inaccessible for placing there a test field. The developed calibration technique estimates parameters of interior orientation of the photogrammetric system and parameters of multimedia optical environment by special preliminary procedure, that allows to obtain accurate 3D measurements during experiments in hydrodynamic tunnel. Experimental evaluation of the developed technique demonstrated high accuracy of photogrammetric 3D measurements.

3D Browsing of Wide‐Angle Fisheye Images Under View‐Dependent Perspective Correction

AbstractThis paper presents a novel technique for 3D browsing of wide‐angle fisheye images using view‐dependent perspective correction (VDPC). First, the fisheye imaging model with interior orientation parameters (IOPs) is established. Thereafter, a VDPC model for wide‐angle fisheye images is proposed that adaptively selects correction planes for different areas of the image format. Finally, the wide‐angle fisheye image is re‐projected to obtain the visual effect of browsing in hemispherical space, using the VDPC model and IOPs of the fisheye camera calibrated using the ideal projection ellipse constraint. The proposed technique is tested on several downloaded internet images with unknown IOPs. Results show that the proposed VDPC model achieves a more uniform perspective correction of fisheye images in different areas, and preserves the detailed information with greater flexibility compared with the traditional perspective projection conversion (PPC) technique. The proposed algorithm generates a corrected image of 512 × 512 pixels resolution at a speed of 58 fps when run on a pure central processing unit (CPU) processor. With an ordinary graphics processing unit (GPU) processor, a corrected image of 1024 × 1024 pixels resolution can be generated at 60 fps. Therefore, smooth 3D visualisation of a fisheye image can be realised on a computer using the proposed algorithm, which may benefit applications such as panorama surveillance, robot navigation, etc.

Topographic Surveying using Low-Cost Amateur Drones & 4K Ultra-High-Definition Videos

These days, the science of photogrammetry is frequently used in a wide range of applications, including engineering geology, medication, security, navigation, and topographic surveying works. Drones have gradually become an effectual and frequent technique for obtaining a number of photogrammetric products, such as ortho-mosaics, Digital Elevation Model (DEM), and land detailed topographic maps. This paper investigates the possibility of employing low-cost amateur drones with 4K Ultra-High-Definition (UHD) video for topographic surveying works and creating DEM's. DJI Mavic 2 PRO drone with Hasselblad L1D-20C 20 MP camera has been used for covering an expected gas & oil pipeline route in the South of Libya with length of nearly 15 km and width of 200 m. 60 well-distributed high-quality 3D ground points, divided as 30 Ground Control Points (GCP's) and 30 check points, have been used for more stable and robust photogrammetric processing and for reliable evaluation, respectively. The camera has been calibrated twice, before and after executing the flight mission for trustworthy Interior Orientation Elements (IOE's). The recorded video has been split into smaller videos based on the capturing time of the required frames, and the small videos have been extracted to individual UHD photos using Matlab image tools. Leica Photogrammetric Suite (LPS) software has been used for all processing steps, except the automatic filtration of the generated tie points, which has been carried out using self-developed Matlab algorithm for epipolar geometry and 2D transformation based filters. Results show that DEM's with quality of nearly 1.2 to 2 decimeter in plane and elevation, respectively can be obtained using DJI Mavic 2 PRO drone, 4K UHD video taken by Hasselblad L1D-20C 20 MP camera, pixel ground footprint of 8 cm, and flight height of 350 m. This level of accuracy is appropriate for many engineering applications, such as initial-planning projects stretched on huge areas, urban development plans, GIS data collection, inventory of earth works materials, and 3D modeling. The obtained quality of the generated DEM depends on the flight height and the camera quality, IOE's, and resolution. Tests show that using 4K UHD video for photogrammetric applications can provide UHD extracted frames, similar to that captured singly, especially with fit flight speed and camera settings, namely: ISO sensitivity, shatter speed, and aperture size. Also, using video facilitates the aerial photography process, overcoming the difficulties of determining the suitable capturing time and location of individual photos in site. The other advantage is the ability of taking alternative frames if the selected images are not suitable for photogrammetric works in terms of tilting and blurring. The opportunity of changing the overlapping rates across the route is another important advantage of using video, especially for curvy routes. Moreover, using different groups of overlapped images for the same route helps for creating different DEM's for the same area, resulting more precise and dense topographic surveying works.

Optimal Self-Calibration Strategies in the Combined Bundle Adjustment of Aerial–Terrestrial Integrated Images

Accurate combined bundle adjustment (BA) is a fundamental step for the integration of aerial and terrestrial images captured from complementary platforms. In traditional photogrammetry pipelines, self-calibrated bundle adjustment (SCBA) improves the BA accuracy by simultaneously refining the interior orientation parameters (IOPs), including lens distortion parameters, and the exterior orientation parameters (EOPs). Aerial and terrestrial images separately processed through SCBA need to be fused using BA. Thus, the IOPs in the aerial–terrestrial BA must be properly treated. On one hand, the IOPs in one flight should be identical for the same images in physics. On the other hand, the IOP adjustment in the cross-platform-combined BA may mathematically improve the aerial–terrestrial image co-registration degree in 3D space. In this paper, the impacts of self-calibration strategies in combined BA of aerial and terrestrial image blocks on the co-registration accuracy were investigated. To answer this question, aerial and terrestrial images captured from seven study areas were tested under four aerial–terrestrial BA scenarios: the IOPs for both aerial and terrestrial images were fixed; the IOPs for only aerial images were fixed; the IOPs for only terrestrial images were fixed; the IOPs for both images were adjusted. The cross-platform co-registration accuracy for the BA was evaluated according to independent checkpoints that were visible on the two platforms. The experimental results revealed that the recovered IOPs of aerial images should be fixed during the BA. However, when the tie points of the terrestrial images are comprehensively distributed in the image space and the aerial image networks are sufficiently stable, refining the IOPs of the terrestrial cameras during the BA may improve the co-registration accuracy. Otherwise, fixing the IOPs is the best solution.

Geometric calibration of a hyperspectral frame camera with simultaneous determination of sensors misalignment

The recent development of lightweight and relatively low-cost hyperspectral sensors has created new perspectives for remote sensing applications. This study aimed to investigate the geometric calibration of a hyperspectral frame camera based on a tuneable Fabry–Pérot interferometer (FPI) and two sensors. The radiation passes through the optics and then through the FPI, where it is redirected to two sensors using a beam-splitting prism. Previous studies have shown significant variations between the interior orientation parameters for the different bands, both between bands of the same sensor and between different sensors, and that these variations are due to the principle of image acquisition. Discrepancies of tens of pixels were obtained by comparing image coordinates measured in different bands. In this research, it was proposed to calibrate this camera in a static mode with changes in the mathematical calibration model. The restriction of obtaining only one set of exterior orientation parameters by hypercube was applied, adding parameters related to the misalignment between the sensors and parameters of a linear function relating the camera principal distance to the wavelength values. The application of the parameters estimated with this approach reduced the discrepancies between image coordinates measured in different bands to values smaller than one pixel. Using the sensor calibration parameters in the mobile UAV operation in an aerial bundle adjustment reduced the root mean square error (RMSE) on checkpoints by approximately 20% compared to the traditional model in which the interior orientation parameters and lens distortions were calibrated for each band separately. Thus, it was possible to obtain accurate results that make the use of this camera more practical since only one set of calibration parameters for all bands is needed.

Comparison Between Multi Epipolar Geometry and Conformal 2d Transformation-Based Filters for Optical Robot Navigation

Abstract: This research paper comes in a series of comparisons between the Multi Epipolar Geometry-based Filter (M-EGF) and the common filters that are extensively used in Optical Robot Navigation (ORN) for filtering the results of Automatic Image Matching (AIM). The accuracy of ORN depends mainly on the quality of the AIM results. Conformal 2D transformation-based Filter (C-2DF) is a common filter used in ORN applications. However, C-2DF is limited in terms of: time processing, disability to deal AIM data with significant number of outliers, dealing with images taking from different Depths Of Fields (DOF) and difficult view angles. M-EGF has been introduced recently by the author and compared with Single Epipolar Geometry based Filter (S-EGF), showing high ability to provide ORN with precise, trusted, outlier-free, and real time observations. In this paper, M-EGF has been compared with C-2DF using images captured by optical navigation system simulating ORN, which includes 3 cameras synchronized using GPS time. The performance of the two filters has been evaluated in different AIM environments and the automatically filtered results have been compared to precisely manually reviewed matched points using Matlap. Tests show that C-2DF has failed to deal with the AIM results in areas with open, narrow, and confused DOF. Also, they have failed to find out the right mathematical model in data with high rate of mismatched points and images with difficult view angles. C-2DF is also limited in its capability to deal with figures including different scales. With limited DOF and limited rate of errors, C-2DF has provided relatively sufficient results, which can be suitable for ORN in terms of quality and processing time. C-2DF overcomes S-EGF and M-EGF in terms of being not affected by errors in the Exterior Orientation Parameters (EOP) and Interior Orientation Elements (IOE) of cameras, as C-2DF depends on estimating the mathematical model parameters using only image points.

Comparison between Multi Epipolar Geometry & Affine 2d Transformation-Based Filters for Optical Robot Navigation

Abstract: In previous research papers, Multi Epipolar Geometry-based Filter (M-EGF) has proven its high capability to overcome Single Epipolar Geometry-based Filter (S-EGF) as well as with conformal 2D Transformation-based Filter (C-2DF) in terms of providing precise, trusted, outlier-free, and real time Automatic Image Matching (AIM) results for Optical Robot Navigation (ORN) applications. This paper comes in a series of comparing M-EGF with the most common filters extensively used in ORN. Affine 2D Transformation-based Filter (A-2DF) is another familiar filter used for detecting outliers in AIM results and regards as the advanced version of C-2DF, where it can deal with any changing or distortion in image scales in X and Y directions. In this paper, M-EGF has been compared with A-2DF using the same system, tests, tracks and images and the same evaluation techniques used for comparing M-EGF to S-EGF and C-2DF in the related research papers. Tests show that A-2DF is similar to C-2DF in terms of its disability to filter AIM results in areas with open, narrow, and confused Depth Of Field (DOF). A-2DF is also limited in terms of finding out the 6 correct parameters for its mathematical model when AIM results includes a significant number of mismatching points. Difficult view angles has also a noted effect on the performance of A-2DF, but less than that of C-2DF due to its ability to work with different scales. In tests including limited DOF and low level of outliers, A-2DF has shown comparatively adequate outcomes, that is suitable for non-sensitive applications, in which the error does not entail any serious consequences. A-2DF is independent of the errors in Exterior Orientation Parameters (EOP) and Interior Orientation Elements (IOE) of cameras, where it is image points dependent estimation filter. Tests show that M-EGF is time- effective, efficient with any AIM findings, regardless DOF, capturing angle, outliers rate in observations, type of features and tracks. Unlike, A-2DF, tests show that the performance of M-EGF can affect by the quality of IOEs and EOPs, as a number of correctly matched points might be rejected, and this limitation is not related to the filter mathematical design but the professionalism of cameras calibration and EOP determination. in terms of processing time, A-2DF is considerably slow comparing with M-EGF due to its iterative and repeatability design, which makes it, unlike MEGF, unsuitable for real-time precise ORN applications. Keywords: Optical Robot Navigation, Epipolar Geometry, Automatic Image Matching, Co-planarity Equation, Real Time Applications, Affine 2D Transformation

Multi and single epipolar geometry-based filters vs. affine and conformal 2D transformation-based filters

The accuracy of Optical Robot Navigation (ORN) depends mainly on the quality of the Automatic Image Matching (AIM) results. Conformal 2D transformation-based Filter (C-2DF), affine 2D transformation-based Filter (A-2DF) and Single Epipolar Geometry-based Filter (S-EGF) are common filters used in ORN. Multi-Epipolar Geometry-based Filter (M-EGF) has been introduced, tested and evaluated by the author and compared by each filter of those mentioned above in separate research papers. This paper comes to include all these individually comparisons, with additional details, showing the advantages and limitations of all filters comparing to each other. Tests show that C-2DF and A-2DF have failed to deal with AIM results in areas with open, narrow, and confused DOF. Also, they have failed to find out the right mathematical model in data with high rate of mismatched points and data obtained from images with difficult view angles. With limited DOF and low rate of errors, C-2DF and A-2DF have provided relatively sufficient results, which can be used for ORN applications that do not require precision. A-2DF is relatively better than C-2DF due to its flexibility to deal with figures including different scales, which is the case when dealing with different levels of DOF and different capturing angles between the cameras. The processing time is another disadvantage of C-2DF and A-2DF, where these are based on iterative estimation methods. Tests display how A-2DF is slowest, which keeps it away from using with real time ORN applications. Tests show that S-EGF and M-EGF are timesaving and able to deal with any AIM results, regardless the DOF, view angle and errors rate in observations. S-EGF is affected in areas including lines parallel to the cameras base line. M-EGF has offered the best results in terms of providing error-free filtered matched points in all tests. This can be attributed to the high restriction level of this filter, where the probability for the mismatched point to pass throughput the three co-planarity equations is nearly zero. M-EGF and S-EGF are affected by the quality of the Interior Orientation Elements (IOEs) and Exterior Orientation Parameters (EOPs) of the three cameras, leading to rejection a small number of corrected matched points, which can be avoided with professionally manufactured ORN systems. Tests illustrated that S-EGF and M-EGF are extremely high-speed and S-EGF is the faster and M-EGF, C-2DF, and A-2DF comes after, respectively. All results indicated that M-EGF is the best, as it is fast, restricted, reliable, and error-free technique and is suitable for real-time precise ORN applications.

The Conception of Test Fields for Fast Geometric Calibration of the FLIR VUE PRO Thermal Camera for Low-Cost UAV Applications.

The dynamic evolution of photogrammetry led to the development of numerous methods of geometric calibration of cameras, which are mostly based on building flat targets (fields) with features that can be distinguished in the images. Geometric calibration of thermal cameras for UAVs is an active research field that attracts numerous researchers. As a result of their low price and general availability, non-metric cameras are being increasingly used for measurement purposes. Apart from resolution, non-metric sensors do not have any other known parameters. The commonly applied process is self-calibration, which enables the determining of the approximate elements of the camera’s interior orientation. The purpose of this work was to analyze the possibilities of geometric calibration of thermal UAV cameras using proposed test field patterns and materials. The experiment was conducted on a FLIR VUE PRO thermal camera dedicated to UAV platforms. The authors propose the selection of various image processing methods (histogram equalization, thresholding, brightness correction) in order to improve the quality of the thermograms. The consecutive processing methods resulted in over 80% effectiveness on average by 94%, 81%, and 80 %, respectively. This effectiveness, for no processing and processing with the use of the filtering method, was: 42% and 38%, respectively. Only high-pass filtering did not improve the obtained results. The final results of the proposed method and structure of test fields were verified on chosen geometric calibration algorithms. The results of fast and low-cost calibration are satisfactory, especially in terms of the automation of this process. For geometric correction, the standard deviations for the results of specific methods of thermogram sharpness enhancement are two to three times better than results without any correction.

Multitemporal UAV Photogrammetry For Sandbank Morphological Change Analysis: Evaluations of Camera Calibration Methods, Co-Registration Strategies, and the Reconstructed DSMs

Sandbank morphology is a longshore corridor with fast morphological changes. To analyze its changes, unmanned aerial vehicle (UAV) is the preferred effective and flexible data collection platform, which can collect very high resolution (VHR) images. UAV campaigns along corridor areas require well-distributed ground control points (GCP) and accurate geotagged image positions to avoid misalignment errors of generated digital surface models (DSMs). However, a sandy terrain can make the measurement of GCPs difficult or even impossible. Furthermore, UAVs are often equipped with consumer-grade devices (onboard GNSS and non-metric cameras), which, therefore, cannot provide accurate image positions and lead to geometric distortions of the image network due to inappropriate lens distortion corrections. This paper proposes a strategy to calibrate the used camera and co-register multi-temporal images without the need for accurate geotagged information on the whole datasets and well-distributed GCPs. The proposed strategy includes two improvements. First, we performed semi-on-the-job self-calibration (semi-OTJSC) using UAV images with favor orientations and flight altitudes to pre-calibrate interior orientation parameters (IOPs) and additional lens distortion parameters. This was then followed by another OTJSC of IOPs involving images with accurate geotagged positions. Second, a “transferred aerial-triangulation (Trans-AT)” strategy is proposed to co-register two consecutive UAV datasets through AT procedure similar to GNSS-supported AT within a strip image block (SIB). The experimental results revealed that the proposed method provides the best-fit camera parameters and generates high-accuracy co-registered DSMs. This strategy contributes significantly to multi-temporal camera calibration and co-registration procedures for determining morphological changes in corridor mapping.

Improvement of an LCD-Based Calibration Site for Reliable Focus-Dependent Camera Calibration

This paper proposes an improved calibration site design for reliable focus-dependent camera calibration in close-range photogrammetry. The proposed calibration site displays an adjustable checkerboard on a large liquid crystal display television (LCD TV), such that the same number of checker cells appear at the same position at any focusing distance. By adjusting the roll and pitch of the LCD TV bracket, various convergent angles were implemented in order to successfully conduct self-calibration. To obtain a greater depth of field and to facilitate reproducibility, the images were acquired using a small aperture from a camera that was firmly mounted on a distance-adjustable rail. The interior orientation parameters of the camera were estimated, and the estimation errors were assessed. The results revealed a flat error level for each focusing distance; thus, consistent reliability was achieved regardless of the focusing distance. Using the estimated parameters, a 3D measurement test was conducted, and the proposed method yielded 2% smaller root mean square error. The proposed method can be implemented easily and performs reliable and accurate focus-dependent calibration.