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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AprilTags and other passive fiducial markers require specialized algorithms to detect markers among other features in a natural scene. The vision processing steps generally dominate the computation time of a tag detection pipeline, so even small improvements in marker detection can translate to a faster tag detection system. We incorporated lessons learned from implementing and supporting the AprilTag system into this improved system. This work describes AprilTag 2, a completely redesigned tag detector that improves robustness and efficiency compared to the original AprilTag system. The tag coding scheme is unchanged, retaining the same robustness to false positives inherent to the coding system. The new detector improves performance with higher detection rates, fewer false positives, and lower computational time. Improved performance on small images allows the use of decimated input images, resulting in dramatic gains in detection speed. 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. It is believed that certain contour attributes, specifically orientation, curvature and linear extent, provide essential cues for object (shape) recognition. The present experiment examined this hypothesis by comparing stimulus conditions that differentially provided such cues. A spaced array of dots was used to mark the outside boundary of namable objects, and subsets were chosen that contained either contiguous strings of dots or randomly positioned dots. These subsets were briefly and successively displayed using an MTDC information persistence paradigm. Across the major range of temporal separation of the subsets, it was found that contiguity of boundary dots did not provide more effective shape recognition cues. This is at odds with the concept that encoding and recognition of shapes is predicated on the encoding of contour attributes such as orientation, curvature and linear extent. We propose a novel approach to both learning and detecting local contour-based representations for mid-level features. Our features, called sketch tokens, are learned using supervised mid-level information in the form of hand drawn contours in images. Patches of human generated contours are clustered to form sketch token classes and a random forest classifier is used for efficient detection in novel images. We demonstrate our approach on both top-down and bottom-up tasks. We show state-of-the-art results on the top-down task of contour detection while being over 200x faster than competing methods. We also achieve large improvements in detection accuracy for the bottom-up tasks of pedestrian and object detection as measured on INRIA and PASCAL, respectively. These gains are due to the complementary information provided by sketch tokens to low-level features such as gradient histograms.