Automated indoor RFID inventorying using a self-guided micro-aerial vehicle

Parkison, Steven A.; Psota, Eric T.; Pérez, Lance C.

doi:10.1109/eit.2014.6871787

Cited by 2 publications

(1 citation statement)

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“…Ramirez [25] has made a dataset for visual odometry and localization in which he used AprilTags as landmarks for accuracy evaluation. Parkison et al [26] have used AprilTag to evaluate the position and pose of a micro aerial vehicle (MAV) for automated indoor RFID inventorying. Raina et al [27] have used multiple AprilTags as a ground truth evaluation system for 3D pose estimation in a cluttered environment.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Improvements in AprilTag Based State Estimation

Abbas

Aslam

Berns

et al. 2019

Sensors

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In this paper, we analyzed the accuracy and precision of AprilTag as a visual fiducial marker in detail. We have analyzed error propagation along two horizontal axes along with the effect of angular rotation about the vertical axis. We have identified that the angular rotation of the camera (yaw angle) about its vertical axis is the primary source of error that decreases the precision to the point where the marker system is not potentially viable for sub-decimeter precise tasks. Other factors are the distance and viewing angle of the camera from the AprilTag. Based on these observations, three improvement steps have been proposed. One is the trigonometric correction of the yaw angle to point the camera towards the center of the tag. Second, the use of a custom-built yaw-axis gimbal, which tracks the center of the tag in real-time. Third, we have presented for the first time a pose-indexed probabilistic sensor error model of the AprilTag using a Gaussian Processes based regression of experimental data, validated by particle filter tracking. Our proposed approach, which can be deployed with all three improvement steps, increases the system's overall accuracy and precision by manifolds with a slight trade-off with execution time over commonly available AprilTag library. These proposed improvements make AprilTag suitable to be used as precision localization systems for outdoor and indoor applications. provide adequate accuracy for tasks that demand sub-meter localization accuracies such as robot navigation, obstacle avoidance or structural inspection in confined environments. Some high-end GPS methodologies such as D-GPS and RKT-GPS have an accuracy of 0.1 meters or less but they are quite expensive and are hard to setup. In outdoor environments, the deployment of fiducial marker-based systems are also possible but they have limitations on operating distance and field-of-view.AprilTag is one of the most commonly used fiducial markers that can be used both indoors and outdoors for ground truth generation in 6-DOF, but with limitations [6]. We have precisely identified these limitations and have explained the source of these limitations with statistical error models. The proposed research has established that both distance and orientation of viewing camera from the target tag effects accuracy. However, uncorrected orientation uncertainty is a more significant source of accuracy degradation. AprilTag's accuracy is maximum when the viewing camera is pointed towards the center of the tag. Moreover, in the current implementation of the AprilTag localization system, this source of error is left unaddressed. As a result, the system suffers from a loss of performance, which is rectifiable. The proposed research has filled this gap (only for 2D) via an empirical analysis of the AprilTag system. Furthermore, a data-driven probabilistic sensor model has also been proposed, which works both in indoor and outdoor environments.In this paper, we have proposed techniques to overcome this limitation and to increase the accuracy even f...

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Section: Introductionmentioning

confidence: 99%