Application of vision based techniques for UAV position estimation
01 May 2016-pp 1-5
TL;DR: Here, vision based navigation is explored with different algorithms (RANSAC feature detection and normalized cross correlation with prior edge detection) and the benefits and disadvantages of each are compared.
Abstract: The objective of this project is to set up wireless communication for Unmanned Aerial Vehicles (UAVs) and to estimate the position of the UAV using vision-based techniques with an onboard camera, in a real-time, continuously interacting scenario. Here, vision based navigation is explored with different algorithms (RANSAC feature detection and normalized cross correlation with prior edge detection) and the benefits and disadvantages of each are compared. Vision based navigation uses a camera onboard the UAV to continuously transmit an aerial video of the ground to the GCS and the position of the UAV is determined using geo-referenced images such as those obtained from Google Earth.
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TL;DR: This paper reviews most of the literature in this field since 2015 and existing approaches are reviewed in 4 categories: template matching, feature points matching, deep learning and visual odometry.
42 citations
01 Nov 2018
TL;DR: Different technologies of vision-based positioning techniques and their core algorithms are discussed and the advantages and disadvantages of different algorithms are compared.
Abstract: With the rapid development of computer vision technology and the wide use of cameras, vision-based positioning has become a new research hot spot. Higher accuracy, lower cost, wider range of applications and some other advantages make vision-based positioning technology more promising than traditional positioning methods. This paper gives a briefly introduction of different methods of positioning firstly. Then we discuss different technologies of vision-based positioning techniques and their core algorithms. In addition, we compared the advantages and disadvantages of different algorithms. Finally, we investigate different applications of vision-based positioning and analyze the existing problems and possible development trends.
6 citations
Cites background from "Application of vision based techniq..."
...Compared with the wheel-type speedometer the greate st advantage of VO is that it is not influenced by unfavorable conditions such as wheel sliding on the rough ground and other and the estimation result is more accurate [20]....
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4 citations
TL;DR: A comprehensive review of vision-based UAV navigation techniques is provided in this article , where existing techniques have been categorized and extensively reviewed with regard to their capabilities and characteristics and then, they are qualitatively compared in terms of various aspects.
Abstract: In recent years, unmanned aerial vehicles (UAVs), commonly known as drones, have gained increasing interest in both academia and industries. The evolution of UAV technologies, such as artificial intelligence, component miniaturization, and computer vision, has decreased their cost and increased availability for diverse applications and services. Remarkably, the integration of computer vision with UAVs provides cutting-edge technology for visual navigation, localization, and obstacle avoidance, making them capable of autonomous operations. However, their limited capacity for autonomous navigation makes them unsuitable for global positioning system (GPS)-blind environments. Recently, vision-based approaches that use cheaper and more flexible visual sensors have shown considerable advantages in UAV navigation owing to the rapid development of computer vision. Visual localization and mapping, obstacle avoidance, and path planning are essential components of visual navigation. The goal of this study was to provide a comprehensive review of vision-based UAV navigation techniques. Existing techniques have been categorized and extensively reviewed with regard to their capabilities and characteristics. Then, they are qualitatively compared in terms of various aspects. We have also discussed open issues and research challenges in the design and implementation of vision-based navigation techniques for UAVs.
3 citations
TL;DR: In this article , the authors examined collaborative visual positioning among multiple UAVs (UAV autonomous positioning and navigation, distributed collaborative measurement fusion under cluster dynamic topology, and group navigation based on active behavior control and distributed fusion of multi-source dynamic sensing information).
Abstract: The employment of unmanned aerial vehicles (UAVs) has greatly facilitated the lives of humans. Due to the mass manufacturing of consumer unmanned aerial vehicles and the support of related scientific research, it can now be used in lighting shows, jungle search-and-rescues, topographical mapping, disaster monitoring, and sports event broadcasting, among many other disciplines. Some applications have stricter requirements for the autonomous positioning capability of UAV clusters, requiring its positioning precision to be within the cognitive range of a human or machine. Global Navigation Satellite System (GNSS) is currently the only method that can be applied directly and consistently to UAV positioning. Even with dependable GNSS, large-scale clustering of drones might fail, resulting in drone cluster bombardment. As a type of passive sensor, the visual sensor has a compact size, a low cost, a wealth of information, strong positional autonomy and reliability, and high positioning accuracy. This automated navigation technology is ideal for drone swarms. The application of vision sensors in the collaborative task of multiple UAVs can effectively avoid navigation interruption or precision deficiency caused by factors such as field-of-view obstruction or flight height limitation of a single UAV sensor and achieve large-area group positioning and navigation in complex environments. This paper examines collaborative visual positioning among multiple UAVs (UAV autonomous positioning and navigation, distributed collaborative measurement fusion under cluster dynamic topology, and group navigation based on active behavior control and distributed fusion of multi-source dynamic sensing information). Current research constraints are compared and appraised, and the most pressing issues to be addressed in the future are anticipated and researched. Through analysis and discussion, it has been concluded that the integrated employment of the aforementioned methodologies aids in enhancing the cooperative positioning and navigation capabilities of multiple UAVs during GNSS denial.
References
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TL;DR: This paper presents work on computing shape models that are computationally fast and invariant basic transformations like translation, scaling and rotation, and proposes shape detection using a feature called shape context, which is descriptive of the shape of the object.
Abstract: We present a novel approach to measuring similarity between shapes and exploit it for object recognition. In our framework, the measurement of similarity is preceded by: (1) solving for correspondences between points on the two shapes; (2) using the correspondences to estimate an aligning transform. In order to solve the correspondence problem, we attach a descriptor, the shape context, to each point. The shape context at a reference point captures the distribution of the remaining points relative to it, thus offering a globally discriminative characterization. Corresponding points on two similar shapes will have similar shape contexts, enabling us to solve for correspondences as an optimal assignment problem. Given the point correspondences, we estimate the transformation that best aligns the two shapes; regularized thin-plate splines provide a flexible class of transformation maps for this purpose. The dissimilarity between the two shapes is computed as a sum of matching errors between corresponding points, together with a term measuring the magnitude of the aligning transform. We treat recognition in a nearest-neighbor classification framework as the problem of finding the stored prototype shape that is maximally similar to that in the image. Results are presented for silhouettes, trademarks, handwritten digits, and the COIL data set.
6,693 citations
TL;DR: An integrated system for navigation parameter estimation using sequential aerial images, where the navigation parameters represent the positional and velocity information of an aircraft for autonomous navigation is presented.
Abstract: Presents an integrated system for navigation parameter estimation using sequential aerial images, where the navigation parameters represent the positional and velocity information of an aircraft for autonomous navigation. The proposed integrated system is composed of two parts: relative position estimation and absolute position estimation. Relative position estimation recursively computes the current position of an aircraft by accumulating relative displacement estimates extracted from two successive aerial images. Simple accumulation of parameter values reduces the reliability of the extracted parameter estimates as an aircraft goes on navigating, resulting in a large positional error. Therefore, absolute position estimation is required to compensate for the positional error generated by the relative position estimation. Absolute position estimation algorithms using image matching and digital elevation model (DEM) matching are presented. In the image matching, a robust-oriented Hausdorff measure (ROHM) is employed, whereas in the DEM matching, an algorithm using multiple image pairs is used. Experiments with four real aerial image sequences show the effectiveness of the proposed integrated position estimation algorithm.
207 citations
Additional excerpts
...If there are two images, A and B, the cross correlation image is defined as follows: ( , ) = ( , ) ( − , − ),, (1) ( , ) = ( , ) − ( − , − ) −,, (2)...
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TL;DR: A vision-based navigation architecture which combines inertial sensors, visual odometry, and registration of the on-board video to a geo-referenced aerial image is proposed which is capable of providing high-rate and drift-free state estimation for UAV autonomous navigation without the GPS system.
Abstract: This paper investigates the possibility of augmenting an Unmanned Aerial Vehicle (UAV) navigation system with a passive video camera in order to cope with long-term GPS outages. The paper proposes a vision-based navigation architecture which combines inertial sensors, visual odometry, and registration of the on-board video to a geo-referenced aerial image. The vision-aided navigation system developed is capable of providing high-rate and drift-free state estimation for UAV autonomous navigation without the GPS system. Due to the use of image-to-map registration for absolute position calculation, drift-free position performance depends on the structural characteristics of the terrain. Experimental evaluation of the approach based on offline flight data is provided. In addition the architecture proposed has been implemented on-board an experimental UAV helicopter platform and tested during vision-based autonomous flights.
169 citations
Additional excerpts
...If there are two images, A and B, the cross correlation image is defined as follows: ( , ) = ( , ) ( − , − ),, (1) ( , ) = ( , ) − ( − , − ) −,, (2)...
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TL;DR: A straight-line extractor that produces line descriptions from aerial images is described, which describes techniques for eliminating many of the physically insignificant lines, given that the domain of interpretation is aerial images dominated by man-made objects.
Abstract: A straight-line extractor that produces line descriptions from aerial images is described. The input to the line extractor is in the form of an edge image, where the contrast and direction of each edge pixel is specified. The system scans the edge image left to right and top to bottom and assigns a line label for each scanned edge pixel, thereby generating a label image. At the end of this process, each edge pixel has a line label associated with it, and edge pixels that belong to the same line will be assigned the same line label. In addition, with each line label, a record that stores the end points, the average contrast, and the pixel support of the line is generated. The label image is used as a spatial index to further link fragmented lines. The authors also describe techniques for eliminating many of the physically insignificant lines, given that the domain of interpretation is aerial images dominated by man-made objects. >
109 citations
TL;DR: Two techniques to control UAVs (Unmanned Aerial Vehicles), based on visual information are presented, based on the detection and tracking of planar structures from an on-board camera and 3D reconstruction of the position of the UAV based on an external camera system.
Abstract: In this paper, two techniques to control UAVs (Unmanned Aerial Vehicles), based on visual information are presented. The first one is based on the detection and tracking of planar structures from an on-board camera, while the second one is based on the detection and 3D reconstruction of the position of the UAV based on an external camera system. Both strategies are tested with a VTOL (Vertical take-off and landing) UAV, and results show good behavior of the visual systems (precision in the estimation and frame rate) when estimating the helicopter's position and using the extracted information to control the UAV.
69 citations
Additional excerpts
...If there are two images, A and B, the cross correlation image is defined as follows: ( , ) = ( , ) ( − , − ),, (1) ( , ) = ( , ) − ( − , − ) −,, (2)...
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