In this paper, we exploit prior information from global positioning systems and inertial measurement units to speed up the process of large scene reconstruction from images acquired by Unmanned Aerial Vehicles. We utilize weak pose information and intrinsic parameter to obtain the projection matrix for each view. As compared to unmanned aerial vehicles' flight altitude, topographic relief can usually be ignored, we assume that the scene is flat and use weak perspective camera to get projective transformations between two views. Furthermore, we propose an overlap criterion and select potentially matching view pairs between projective transformed views. A robust global structure from motion method is used for image based reconstruction. Our real world experiments show that the approach is accurate, scalable and computationally efficient. Moreover, projective transformations between views can also be used to eliminate false matching.
As a category of special control points which can be automatically identified, artificial coded targets have been widely developed in the field of computer vision, photogrammetry, augmented reality, etc. In this paper, a new circular coded target designed by RockeTech technology Corp. Ltd is analyzed and studied, which is called circular coded diagonal target (CCDT). A novel detection and recognition method with good robustness is proposed in the paper, and implemented on Visual Studio. In this algorithm, firstly, the ellipse features of the center circle are used for rough positioning. Then, according to the characteristics of the center diagonal target, a circular frequency filter is designed to choose the correct center circle and eliminates non-target noise. The precise positioning of the coded target is done by the correlation coefficient fitting extreme value method. Finally, the coded target recognition is achieved by decoding the binary sequence in the outer ring of the extracted target. To test the proposed algorithm, this paper has carried out simulation experiments and real experiments. The results show that the CCDT recognition and accurate locating method proposed in this paper can robustly recognize and accurately locate the targets in complex and noisy background.
Because of the ability to optimize the 3D points and viewing parameters jointly and simultaneously, Sparse Bundle Adjustment (SBA) is an essential procedure and usually used as the last step of Structure from Motion (SFM). Recent development of SBA is incline to research on combination of the numeric method with matrix compression technique for more efficient and less memory consuming, and of prior information with SBA for the high accuracy. In this paper, a new hard constrained SBA method for multi-camera is presented. This method takes the prior information of 3D model or multi-camera into account as a hard constraint, and its solution is accomplished by the Lagrange multiplier method and Schur complement combined and with block matrix. The contribution of this work is that it provides a solution integrate constraint and multi-camera SBA, which is desired in the SFM problem and photogrammetry area. Another noticeable aspect is that obvious less time consuming with block matrix based than without, and the accuracy is maintained.
The bearing only tracking problem is to measure the position and velocity of moving points from a moving camera. The method in this paper mainly solves the measurement problem of a single camera or some non-synchronized cameras. This paper introduces the process model and basic method of bearing only tracking problem, and the different optimization methods based on it, then compares the advantages and disadvantages of those optimization methods. The research method of this kind of problem is discussed in the last of this paper.
Based on the principle of videometrics, this paper presents a visual positioning and navigation system for lunar soil sampling and encapsulation. The system uses local histogram technique to decrease the influence of complex light environment and sub-pixel correlation technique of camera measurement is used to overcome the influence of low resolution of monitor camera and improve the precision of measurement. It can provide pose parameters for space manipulator operation.
When videogammetry (optical measurement) was carried outdoor or under cruel indoor circumstance, the results would be inevitably affected by the atmosphere turbulence. As a result, the precision of surveying was destroyed. The field of air turbulence’s impact on optical measurement was neglected by scholars for a long time, the achievements massed about laser optics and optical communications. The mostly adapted method was noise filtration when the pixel wandering could not be rejected in engineering application, which got little improvement on usual conditions. The principle of influence under atmospheric turbulence on optical measurement is presented in this paper. And experiments data and applications are carried out to announce the impact of atmospheric turbulence. Combining with relevant researches, some essential issues and expectations of the atmospheric turbulence research are proposed.
In order to fully navigate using a vision sensor, a 3D edge model based detection and tracking technique was developed. Firstly, we proposed a target detection strategy over a sequence of several images from the 3D model to initialize the tracking. The overall purpose of such approach is to robustly match each image with the model views of the target. Thus we designed a line segment detection and matching method based on the multi-scale space technology. Experiments on real images showed that our method is highly robust under various image changes. Secondly, we proposed a method based on 3D particle filter (PF) coupled with M-estimation to track and estimate the pose of the target efficiently. In the proposed approach, a similarity observation model was designed according to a new distance function of line segments. Then, based on the tracking results of PF, the pose was optimized using M-estimation. Experiments indicated that the proposed method can effectively track and accurately estimate the pose of freely moving target in unconstrained environment.
Satellite-rocket docking ring recognition method based on mathematical morphology is presented in this paper, according to the geometric and grayscale characteristics of the docking ring typical structure. The docking ring used in this paper is a circle with a cross in the middle. Most of spacecrafts are transported into orbit by rocket, and they retain the connection component with the rocket. The tracing spacecraft should capture the target spacecraft first before operating the target spacecraft. The docking ring is one of the typical parts of a spacecraft, and it can be recognized automatically. Thereby we can capture the spacecraft through the information of the docking ring. Firstly a multi-step mathematical morphology processing is applied to the image of the target spacecraft with different structure element, followed by edge detection and line detection, and finally docking ring typical structure is located in the image by relative geometry analysis. The images used in this paper are taken of real satellite in lab. The docking ring can be recognized when the distance between the two spacecraft is different. The results of physical simulation experiment show that the method in this paper can recognize docking ring typical structure accurately when the tracing spacecraft is approaching the target spacecraft.
A systematic videometrics method of cooperative object pose-measurement for RVD (rendezvous and docking) is proposed in the paper. According to the method, initial values of pose parameters are calculated from binocular images respectively, and then optimized with bundle adjustment. While a certain variation of some exterior parameters of one camera are added as systematic disturbance purposely, the correct result could be calculated theoretically as the method analysis, and then also is verified in our experiment. The correct result could be converged quickly and stably in the experiment, and accurate pose-measurement results also could be obtained while initial values are provided with some certain errors. Even some amount of disturbance has been added purposely in experiment, high-precision pose results are also obtained by the binocular and bundle adjustment way.
Videometrics is a technique for measuring displacement, deformation and motion with features of precision, multifunctional, automation and real time measurement etc. Videometrics with camera networks is a fast developed area for deformation measurements of large scale structures. Conventional camera network is parallel network where cameras are independent each other and the relations among the cameras are calibrated from their target images. In recent years, we proposed and developed two kinds of videometrics with camera series networks where cameras are connected each other in series and relations among the cameras can be relayed one by one for the deformation measurements of large and super large scale structures. In this paper, our research work in both the camera series and parallel networks for the deformation measurements of large scale structures are overviewed and some new development are introduced. First, our proposed methods of camera series networks are introduced, including the pose-relay videometrics with camera series and the displacement-relay videometrics with camera series. Then our work of large scale structure deformation measurement by camera parallel networks is overviewed. Videometrics with various types of camera networks has the broad prospect of undertaking automatic, long-term and continuous measurement for deformation in engineering projects such as wind turbine blades, ship, railroad beds, and bridges.
This paper proposes a framework for small infrared target real-time visual enhancement. The framework is consisted of three parts: energy accumulation for small infrared target enhancement, noise suppression and weighted fusion. Dynamic programming based track-before-detection algorithm is adopted in the energy accumulation to detect the target accurately and enhance the target’s intensity notably. In the noise suppression, the target region is weighted by a Gaussian mask according to the target’s Gaussian shape. In order to fuse the processed target region and unprocessed background smoothly, the intensity in the target region is treated as weight in the fusion. Experiments on real small infrared target images indicate that the framework proposed in this paper can enhances the small infrared target markedly and improves the image’s visual quality notably. The proposed framework outperforms tradition algorithms in enhancing the small infrared target, especially for image in which the target is hardly visible.
This paper describes an approach for small infrared (IR) target detection using frequency-spatial cues. We model the background as spikes of the amplitude spectrum in the frequency domain. Target regions are highlighted through background suppression, and the suppression is realized via convoluting the amplitude spectrum with a low-pass Gaussian kernel of an appropriate scale. A theoretical analysis of the convoluting process in the frequency domain is presented. We note that the high values are attributed to sharp gradients in the IR image. In order to uniformly highlight the target region, the proposed algorithm introduces cues of image segmentation in the spatial domain. Targets are completely preserved in the final result. An image database is built, which is used to test the proposed algorithm. Results show that our algorithm detects small IR targets effectively with a competitive performance over some state-of-the-art techniques, even for images with cluttered backgrounds. In addition, we show that it is able to detect multiple targets with varied sizes, which are challenges for existing algorithms.
The automatic detection of visually salient information from abundant video imagery is crucial, as it plays an important role in surveillance and reconnaissance tasks for Unmanned Aerial Vehicle (UAV). A real-time approach for the detection of salient objects on road, e.g. stationary and moving vehicle or people, is proposed, which is based on region segmentation and saliency detection within related domains. Generally, the traditional method specifically depends upon additional scene information and auxiliary thermal or IR sensing for secondary confirmation. However, this proposed approach can detect the interesting objects directly from video imagery captured by optical camera fixed on the small level UAV platform. To validate this proposed salient object detection approach, the 25 Hz video data from our low speed small UAV are tested. The results have demonstrated the proposed approach performs excellently in isolated rural environments.
Approaches designed thus far for illumination invariant change detection are generally based on illumination compensation (IC) or illumination invariant descriptors. However, IC cannot handle local illumination variation, and is prone to sacrifice discriminability; illumination invariant descriptors cannot work very robustly in a textureless scenario. To address these problems, we present a novel change detection method by combining local binary pattern (LBP) with local illumination compensation (LIC). Although both LBP and LIC have disadvantages themselves, their independence enables mutual compensation of their disadvantages. Through a reasonable compositional strategy that makes best use of the advantages and bypasses the disadvantages, the proposed method can efficiently handle not only global but also local illumination variation in both textured and texture-less scenario. Experimental results using many real and synthetic images clearly justify our method.
Fast and reliable three-dimensional (3-D) measurement of large stack yards is an important job in bulk load-and-unload operations and logistics management. Traditional noncontacting methods, such as LiDAR and photogrammetry, witness difficulties of complex and irregular shape, single texture and weak reflectivity, and so on. In this paper, we propose a videogrammetry and projected-contour scanning method. The surface of a stack yard can be scanned easily by a laser-line projector, and its 3-D shape can be reconstructed automatically by stereo cameras. There are two main technical contributions of this method: 1. corresponding-point matching in stereo imagery based on image gradient and epipolar line; and 2. single projected-contour extraction under constraint of homography and RANSAC (random sampling consensus). The proposed method has been tested by 3-D-reconstruction experiments of sand tables in indoor and outdoor conditions, which showed that about five contours were reconstructed per second on average, and moving-distance error of a standard slab was less than 0.4 mm in the worst direction of the videogrammetric system. In conclusion, the proposed method is effective for 3-D shape measurement of stack yards in a fast, reliable and accurate way.
We present a videometric method and system to implement terminal guidance for Unmanned Aerial Vehicle(UAV)
accurate landing. In the videometric system, two calibrated cameras attached to the ground are used, and a calibration
method in which at least 5 control points are applied is developed to calibrate the inner and exterior parameters of the
cameras. Cameras with 850nm spectral filter are used to recognize a 850nm LED target fixed on the UAV which can
highlight itself in images with complicated background. NNLOG (normalized negative laplacian of gaussian) operator is
developed for automatic target detection and tracking. Finally, 3-D position of the UAV with high accuracy can be
calculated and transfered to control system to direct UAV accurate landing. The videometric system can work in the rate
of 50Hz. Many real flight and static accuracy experiments demonstrate the correctness and veracity of the method
proposed in this paper, and they also indicate the reliability and robustness of the system proposed in this paper. The
static accuracy experiment results show that the deviation is less-than 10cm when target is far from the cameras and lessthan
2cm in 100m region. The real flight experiment results show that the deviation from DGPS is less-than 20cm. The
system implement in this paper won the first prize in the AVIC Cup-International UAV Innovation Grand Prix, and it is
the only one that achieved UAV accurate landing without GPS or DGPS.
As a rising navigation technology, vision navigation has many advantages, such as passive measurement, antiinterference,
no accumulation of error and comprehensive parameters, etc. It shows a promising application prospects in
autonomous navigation for UAV. Based on an efficient, reliable and accurate scene matching, a vision altimeter and 3-D
position estimation method are proposed. By matching multiple points between aerial image and reference image, it
estimates UAV's position and height according to photogrammetry. To measure UAV's velocity, a mapless speed
measurement method which tracks ground features between image sequences is introduced. Flight tests had shown the
effectiveness and accuracy of our methods.
Methods of measuring a RVD (rendezvous and docking) cooperative object's pose from monocular and binocular
images respectively are presented. The methods solve the initial values first and optimize the object pose parameters by
bundle adjustment. In the disturbance-rejecting binocular method, chosen measurement system parameters of one
camera's exterior parameters are modified simultaneously. The methods need three or more cooperative target points to
measure the object's pose accurately. Experimental data show that the methods converge quickly and stably, provide
accurate results and do not need accurate initial values. Even when the chosen measurement system parameters are
subjected to some amount of disturbance, the binocular method manages to provide fairly accurate results.
An algorithm of multiple facula targets recognition based on edge and region search in full field of a frame of image is
presented. Firstly, the image is segmented by binarization and the burr around the targets is removed by morphology
processing. Then every facula target's edge and region is found and numbered in turn. Experimental results on simulated
images and real images are shown to validate the presented algorithm.
Moving target tracking is a basic task in the processing of high speed photography. Despite its widely applications,
Correlation tracking method can not adapt to the rotation and zoom of target and results in accumulation of tracking
error. The Least Squares Image Matching(LSIM) method which is used in photogrammetry is introduced to moving
target tracking, and a Weighted Least Squares Image Matching(WLSIM) based tracking algorithm is proposed. The
WLSIM based algorithm sets weights according to the target's shape for the Least-Squares Image Matching Algorithm,
as a result matching error produced by the background in the tracking window can be avoided. Experimental results are
shown to demonstrate the robustness, efficiency and accuracy of the proposed algorithm.