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.
After obtaining raw tag pose estimates from the AprilTag detector, our approach uses a recursive least squares (RLS) filter to obtain a common tag bundle pose estimate. A relevant previous work on this topic is @cite_55 in which a particle filter is applied to raw tag detections and RLS is compared to RANSAC for bundle pose estimation. Because the demonstrated accuracy improvement is marginal (about 1 cm in mean position and negligible in mean attitude) and RANSAC has disadvantages like ad hoc threshold settings and a non-deterministic runtime, we prefer RLS for its simplicity. Nevertheless, RANSAC and its more advanced variations @cite_17 can be substituted into our implementation. Other work investigated fusing tag measurements in RGB space with a depth component @cite_25 with impressive gains in accuracy. One can imagine this approach benefiting landing accuracy at low altitudes, however downward-facing stereo camera mounting on drones raises several concerns like weight, vibration effects and space availability.
@cite_55 @cite_25 @cite_17
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Given the advancing importance for light-weight production materials an increase in automation is crucial. This paper presents a prototypical setup to obtain a precise pose estimation for an industrial manipulator in a realistic production environment. We show the achievable precision using only a standard fiducial marker system (AprilTag) and a state-of-the art camera attached to the robot. The results obtained in a typical working space of a robot cell of about 4.5m × 4.5m are in the range of 15mm to 35mm compared to ground truth provided by a laser tracker. We then show several methods of reducing this error by applying state-of-the-art optimization techniques, which reduce the error significantly to less than 10mm compared to the laser tracker ground truth data and at the same time remove e×isting outliers. Although there is an abundance of planar fiducial-marker systems proposed for augmented reality and computer-vision purposes, using them to estimate the pose accurately in robotic applications where collected data are noisy remains a challenge. This is inherently a difficult problem because these fiducial marker systems work solely within the RGB image space and the resolution of cameras on robots is often constrained. As a result, small noise in the image would cause the tag's detection process to produce large pose estimation errors. This paper describes an algorithm that improves the pose estimation accuracy of square fiducial markers in difficult scenes by fusing information from RGB and depth sensors. The algorithm retains the high detection rate and low false positive rate characteristics of fiducial systems while making them much more robust to size, lighting and sensory noise for pose estimation. The improvements make the fiducial tags suitable for robotic tasks requiring high pose accuracy in the real world environment.