An In-Orbit Stereo Navigation Camera Self-Calibration Method for Planetary Rovers with Multiple Constraints

In order to complete the high-precision calibration of the planetary rover navigation camera using limited initial data in-orbit, we proposed a joint adjustment model with additional multiple constraints. Specifically, a base model was first established based on the bundle adjustment model, second-order radial and tangential distortion parameters. Then, combining the constraints of collinearity, coplanarity, known distance and relative pose invariance, a joint adjustment model was constructed to realize the in orbit self-calibration of the navigation camera. Given the problem of directionality in line extraction of the solar panel due to large differences in the gradient amplitude, an adaptive brightness-weighted line extraction method was proposed. Lastly, the Levenberg-Marquardt algorithm for nonlinear least squares was used to obtain the optimal results. To verify the proposed method, field experiments and in-orbit experiments were carried out. The results suggested that the proposed method was more accurate than the self-calibration bundle adjustment method, CAHVOR method (a camera model used in machine vision for three-dimensional measurements), and vanishing points method. The average error for the flag of China and the optical solar reflector was only 1 mm and 0.7 mm, respectively. In addition, the proposed method has been implemented in China’s deep space exploration missions.

Accuracy Assessment for points coordinates surveyed using low-cost Unmanned Aerial Vehicle and Global Positioning System with 3Dsurvey and 3DF Zephyr software

Ground Control Points GCPs are the only way to obtain accurate positions in aerial surveys. At least three points should be utilized, and the model will get increasingly accurate in X, Y, and Z coordinates as the number rises. The accuracy of the 3D model created from aerial photography is also affected by the arrangement of GCPs. The goal of this research is to determine the optimal number and arrangement of GCPs in order to obtain the lowest possible error in point positioning. A conventional UAV called DJI Mavic 2 pro was used to photograph one and a half square kilometer site at an elevation equal to hundred meters from earth’s surface with nadir camera configuration. GSD (ground sampling distance) of 2.3 centimeters was used to collect 1515 pictures. 62 GCPs were observed in PPK (Post Processing kinematic) method using a DGPS (differential global positioning system) receiver GS 15 from Leica. The study area was split into two areas, one with a straight arrangement of GCPs and the other with a diagonal arrangement of GCPs. The pictures were processed using 3Dsurvey and 3DF Zephyr software utilizing a full bundle adjustment procedure with increasing GCPs number beginning with three GCPs and ending with twenty-six GCPs for both arrangement layout, with the other points serving as check points for the model’s accuracy at each attempt. The check point coordinates obtained were compared to the DGPS coordinates. The result indicates the optimal GCP number needed for the most accurate position and spread layout. That the minimum gap between adjacent GCPs ought to be not over than 100 meters and spread homogenously.

Underexposed Vision-Based Sensors’ Image Enhancement for Feature Identification in Close-Range Photogrammetry and Structural Health Monitoring

This paper describes an alternative structural health monitoring (SHM) framework for low-light settings or dark environments using underexposed images from vision-based sensors based on the practical implementation of image enhancement algorithms. The proposed framework was validated by two experimental works monitored by two vision systems under ambient lights without assistance from additional lightings. The first experiment monitored six artificial templates attached to a sliding bar that was displaced by a standard one-inch steel block. The effect of image enhancement in the feature identification and bundle adjustment integrated into the close-range photogrammetry were evaluated. The second validation was from a seismic shake table test of a full-scale three-story building tested at E-Defense in Japan. Overall, this study demonstrated the efficiency and robustness of the proposed image enhancement framework in (i) modifying the original image characteristics so the feature identification algorithm is capable of accurately detecting, locating and registering the existing features on the object; (ii) integrating the identified features into the automatic bundle adjustment in the close-range photogrammetry process; and (iii) assessing the measurement of identified features in static and dynamic SHM, and in structural system identification, with high accuracy.

3-D modelling and visualization of large building using photogrammetric approach

The progress in modern technologies such as precise lightweight cameras mounted on unmanned aerial vehicles (UAV) and the more user-friendly software in the photogrammetric field, allows for 3-D model construction of any structure or shape. Software now achieves in sequence the processes of matching, generating tie points, block bundle adjustment, and generating digital elevation models.The aim of this study is to make a virtual 3-D model of the college of engineering /University of Duhok. Kurdistan Region, Iraq. The data input is vertical and oblique imagery acquired by UAV, ground control points distributed on the surrounded ground, facades, and roof. Ground control points were measured by the GPS RTK system in addition to the reflectorless total station instrument. The data is processed mainly using Agisoft PhotoScan software as well as the Global Mapper and the ReCap software. The output is a 3-D model, digital elevation model, and orthomosaic.Geometric and visual inspections were carried out. Some imperfections appeared on the sharp edges and parapets of the building. In the geometric accuracy of selected points on the building, the maximum standard deviation in the coordinates was ±4cm. The relative accuracy in distance measurements were in the range of 0.72% to 4.92 %

Thallo – Scheduling for High-Performance Large-Scale Non-Linear Least-Squares Solvers

Large-scale optimization problems at the core of many graphics, vision, and imaging applications are often implemented by hand in tedious and error-prone processes in order to achieve high performance (in particular on GPUs), despite recent developments in libraries and DSLs. At the same time, these hand-crafted solver implementations reveal that the key for high performance is a problem-specific schedule that enables efficient usage of the underlying hardware. In this work, we incorporate this insight into Thallo, a domain-specific language for large-scale non-linear least squares optimization problems. We observe various code reorganizations performed by implementers of high-performance solvers in the literature, and then define a set of basic operations that span these scheduling choices, thereby defining a large scheduling space. Users can either specify code transformations in a scheduling language or use an autoscheduler. Thallo takes as input a compact, shader-like representation of an energy function and a (potentially auto-generated) schedule, translating the combination into high-performance GPU solvers. Since Thallo can generate solvers from a large scheduling space, it can handle a large set of large-scale non-linear and non-smooth problems with various degrees of non-locality and compute-to-memory ratios, including diverse applications such as bundle adjustment, face blendshape fitting, and spatially-varying Poisson deconvolution, as seen in Figure 1. Abstracting schedules from the optimization, we outperform state-of-the-art GPU-based optimization DSLs by an average of 16× across all applications introduced in this work, and even some published hand-written GPU solvers by 30%+.

Camera Self-Calibration with GNSS Constrained Bundle Adjustment for Weakly Structured Long Corridor UAV Images

Camera self-calibration determines the precision and robustness of AT (aerial triangulation) for UAV (unmanned aerial vehicle) images. The UAV images collected from long transmission line corridors are critical configurations, which may lead to the “bowl effect” with camera self-calibration. To solve such problems, traditional methods rely on more than three GCPs (ground control points), while this study designs a new self-calibration method with only one GCP. First, existing camera distortion models are grouped into two categories, i.e., physical and mathematical models, and their mathematical formulas are exploited in detail. Second, within an incremental SfM (Structure from Motion) framework, a camera self-calibration method is designed, which combines the strategies for initializing camera distortion parameters and fusing high-precision GNSS (Global Navigation Satellite System) observations. The former is achieved by using an iterative optimization algorithm that progressively optimizes camera parameters; the latter is implemented through inequality constrained BA (bundle adjustment). Finally, by using four UAV datasets collected from two sites with two data acquisition modes, the proposed algorithm is comprehensively analyzed and verified, and the experimental results demonstrate that the proposed method can dramatically alleviate the “bowl effect” of self-calibration for weakly structured long corridor UAV images, and the horizontal and vertical accuracy can reach 0.04 m and 0.05 m, respectively, when using one GCP. In addition, compared with open-source and commercial software, the proposed method achieves competitive or better performance.