Recent applications of unmanned aerial systems (UAS) to precision agriculture have shown increased ease and efficiency in data collection at precise remote locations. However, further enhancement of the field requires operation over long periods of time, e.g. days or weeks. This has so far been impractical due to the limited flight times of such platforms and the requirement of humans in the loop for operation. To overcome these limitations, we propose a fully autonomous rotorcraft UAS that is capable of performing repeated flights for long-term observation missions without any human intervention. We address two key technologies that are critical for such a system: full platform autonomy to enable mission execution independently from human operators and the ability of vision-based precision landing on a recharging station for automated energy replenishment. High-level autonomous decision making is implemented as a hierarchy of master and slave state machines. Vision-based precision landing is enabled by estimating the landing pad's pose using a bundle of AprilTag fiducials configured for detection from a wide range of altitudes. We provide an extensive evaluation of the landing pad pose estimation accuracy as a function of the bundle's geometry. The functionality of the complete system is demonstrated through two indoor experiments with a duration of 11 and 10.6 hours, and one outdoor experiment with a duration of 4 hours. The UAS executed 16, 48 and 22 flights respectively during these experiments. In the outdoor experiment, the ratio between flying to collect data and charging was 1 to 10, which is similar to past work in this domain. All flights were fully autonomous with no human in the loop. To our best knowledge this is the first research publication about the long-term outdoor operation of a quadrotor system with no human interaction.
Our approach, however, is not limited to AprilTags, which can be substituted or combined with other markers for specific applications. A vast number of markers is available targeting different use-cases @cite_42 @cite_34 @cite_12 @cite_15 . Patterns include letters, circles, concentric circles and or polygons, letters, 2 dimensional barcodes and special patterns based on topological @cite_41 , detection range maximizing @cite_14 and blurring occlusion robustness considerations @cite_53 .
@cite_14 @cite_41 @cite_53 @cite_42 @cite_15 @cite_34 @cite_12
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We describe the design and implementation of a fiducial marker system that encodes data in the frequency spectrum of a synthetic image. This distinctive approach to marker synthesis and data encoding allows for partial data extraction in adverse imaging conditions, and can significantly extend the detection range through graceful data degradation. Additional digital encoding and image construction techniques are used to increase the payload capacity, and also to store 3-D pose information in each fiducial marker. This fiducial marker scheme can be configured to match the needs of the target application. We present several experiments investigating the practical range of various parameters as well as the performance of a specific instance of the system. This paper describes reacTIVision: a camera based two dimensional fiducial (marker) tracking system developed for the reacTable*, a table based tangible musical instrument. Key features of reacTIVision include: (1) the ability to track a large number of fiducials with faster than real-time performance and (2) fiducial size may be varied depending on the number of distinct fiducial identities required. We describe recent advances in our implementation of topologybased fiducial recognition, including a generalised method for accurately computing fiducial location and orientation. A method of graph naming known as left heavy depth sequences is applied to the identification of topologically distinct fiducials. Also discussed is our approach to generating fiducial images for the system, in which we employ evolutionary computation to produce compact fiducials with specific geometric properties. Artificial markers are successfully adopted to solve several vision tasks, ranging from tracking to calibration. While most designs share the same working principles, many specialized approaches exist to address specific application domains. Some are specially crafted to boost pose recovery accuracy. Others are made robust to occlusion or easy to detect with minimal computational resources. The sheer amount of approaches available in recent literature is indeed a statement to the fact that no silver bullet exists. Furthermore, this is also a hint to the level of scholarly interest that still characterizes this research topic. With this paper we try to add a novel option to the offer, by introducing a general purpose fiducial marker which exhibits many useful properties while being easy to implement and fast to detect. The key ideas underlying our approach are three. The first one is to exploit the projective invariance of conics to jointly find the marker and set a reading frame for it. Moreover, the tag identity is assessed by a redundant cyclic coded sequence implemented using the same circular features used for detection. Finally, the specific design and feature organization of the marker are well suited for several practical tasks, ranging from camera calibration to information payload delivery. Nowadays, quadrotor is playing a growing number of critical roles in a great many of aspects. A crucial ability is the autonomous landing ability onto a target such as a fixed platform, moving platform, or ship deck platform, employing on-board vision. Researching autonomous landing technologies employing vision for quadrotor have grown up to be the second active area of research over the past years. This article primarily gives the foremost research groups involved in the development of on-board vision autonomous landing technologies for quadrotor, then it discusses the specifics of two factors are taken into account for a smooth landing: sensing and control. The aim of this article is to examine the state-of-the art autonomous landing technologies employing on-board vision that capture entire milestones and seminal works. Last but not least, the article stands out three challenging parts in this research field. While the use of naturally-occurring features is a central focus of machine perception, artificial features (fiducials) play an important role in creating controllable experiments, ground truthing, and in simplifying the development of systems where perception is not the central objective. We describe a new visual fiducial system that uses a 2D bar code style “tag”, allowing full 6 DOF localization of features from a single image. Our system improves upon previous systems, incorporating a fast and robust line detection system, a stronger digital coding system, and greater robustness to occlusion, warping, and lens distortion. While similar in concept to the ARTag system, our method is fully open and the algorithms are documented in detail. In this paper, we present an onboard monocular vision system for autonomous takeoff, hovering and landing of a Micro Aerial Vehicle (MAV). Since pose information with metric scale is critical for autonomous flight of a MAV, we present a novel solution to six degrees of freedom (DOF) pose estimation. It is based on a single image of a typical landing pad which consists of the letter "H" surrounded by a circle. A vision algorithm for robust and real-time landing pad recognition is implemented. Then the 5 DOF pose is estimated from the elliptic projection of the circle by using projective geometry. The remaining geometric ambiguity is resolved by incorporating the gravity vector estimated by the inertial measurement unit (IMU). The last degree of freedom pose, yaw angle of the MAV, is estimated from the ellipse fitted from the letter "H". The efficiency of the presented vision system is demonstrated comprehensively by comparing it to ground truth data provided by a tracking system and by using its pose estimates as control inputs to autonomous flights of a quadrotor. Fiducial marker systems consist of patterns that are mounted in the environment and automatically detected in digital camera images using an accompanying detection algorithm. They are useful for augmented reality (AR), robot navigation, and general applications where the relative pose between a camera and object is required. Important parameters for such marker systems is their false detection rate (false positive rate), their inter-marker confusion rate, minimal detection size (in pixels) and immunity to lighting variation. ARTag is a marker system that uses digital coding theory to get a very low false positive and inter-marker confusion rate with a small required marker size, employing an edge linking method to give robust lighting variation immunity. ARTag markers are bi-tonal planar patterns containing a unique ID number encoded with robust digital techniques of checksums and forward error correction (FEC). This proposed new system, ARTag has very low and numerically quantifiable error rates, does not require a grey scale threshold as does other marker systems, and can encode up to 2002 different unique ID's with no need to store patterns. Experimental results are shown validating this system.