AprilTag: A robust and flexible visual fiducial system

Olson, Edwin

doi:10.1109/icra.2011.5979561

Cited by 1,314 publications

(769 citation statements)

References 21 publications

Supporting

Mentioning

768

Contrasting

Unclassified

Order By: Relevance

“…To evaluate the accuracy of the proposed methods, ground truth position and orientation estimates have been obtained using an external camera to track a fiducial marker attached to the quadrotor. The fiducial marker tracking method, referred to as AprilTags [12], is capable of providing highly accurate, full six degree of freedom (6DOF) pose estimates in real time. Figure 2 shows the AprilTag used for visual tracking mounted to the top of the quadrotor.…”

Section: Methodsmentioning

confidence: 99%

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

Parkison

Psota

Pérez

2014

IEEE International Conference on Electro/Information Technology

View full text Add to dashboard Cite

Abstract-A Micro-Aerial Vehicle (MAV) system is presented for automated indoor inventorying using passive radio frequency identification (RFID). This system utilizes a combination of onboard sensing and processing in order to achieve flight stabilization, generate floor maps, and perform path planning. The proposed state estimation method along with a control feedback strategy are demonstrated to be sufficiently accurate for operation in the indoor environment and the proposed path planning technique is shown to be effective for guiding the MAV while it performs automated RFID scanning.

show abstract

Section: Methodsmentioning

confidence: 99%

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

Parkison

Psota

Pérez

2014

IEEE International Conference on Electro/Information Technology

View full text Add to dashboard Cite

show abstract

“…This requirement has posed a significant challenge for ARC because even by using current state-of-the-art simultaneous localization and mapping (SLAM) techniques, such accuracy is hard to achieve at large scales [12]. In order to address this issue, the authors chose to use computer-vision-based pose estimation algorithms that can achieve high accuracy locally around a visual marker [13,14].…”

Section: Previous Workmentioning

confidence: 99%

“…As pointed out in [11], this requires the localization accuracy to be at least at centimeter level, which is far from achievable using state-of-the-art SLAM style techniques for mobile robots. In order to address this challenge, the authors propose a convenient and accurate solution using planar marker-based pose estimation [14,13], as shown in Figure 6. By 1) attaching fiducial markers at appropriate locations on-site where building tasks are to be performed, 2) surveying their poses m b T in the building reference frame using a total station, and 3) storing these poses inside the system's database, a mobile robot can readily estimate its base's pose b r T inside the building reference frame using equation (3) whenever its on-board camera detects such a marker, based on previous calibration results:…”

Section: Rapid Setup Of Building Reference Framementioning

confidence: 99%

“…This 3D rig was made by attaching 18 Apriltags [14] on two intersecting planes forming a 90 degree angle (as shown in the top of Figure 3) so that the 3D coordinate X of each Apriltag's center could be readily measured. The process of calibration was then simply taking a sequence of N images of the rig and inputting them into the author-developed camera calibration tool 4 , which takes advantage of the Apriltag detection algorithm to detect the 2D image coordinate U of each tag center and establish correspondence with X.…”

Section: Figure 3 Intrinsic Calibration Of Cameramentioning

confidence: 99%

See 1 more Smart Citation

Towards Autonomous Robotic In-Situ Assembly on Unstructured Construction Sites Using Monocular Vision

Feng¹,

Xiao²,

Willette³

et al. 2014

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

View full text Add to dashboard Cite

-Unlike robotics in the manufacturing industry, on-site construction robotics has to consider and address two unique challenges: 1) the rugged, evolving, and unstructured environment of typical work sites; 2) the reversed spatial relationship between the product and the manipulator, i.e. the manipulator has to travel to and localize itself at the work face, rather than a partially complete product arriving at an anchored manipulator. The presented research designed and implemented algorithms that address these challenges and enable autonomous robotic assembly of freeform modular structures on construction sites. Building on the authors' previous work in computer-vision-based pose estimation, the designed algorithms enable a mobile robotic manipulator to: 1) autonomously identify and grasp prismatic building components (e.g., bricks, blocks) that are typically non-unique and arbitrarily stored on-site; and 2) assemble these components into predesigned modular structures. The algorithms use a single camera and a visual marker-based metrology to rapidly establish local reference frames and to detect staged building components. Based on the design of the structure being assembled, the algorithms automatically determine the assembly sequence. Implemented using a 7-axis KUKA KR100 robotic manipulator, the presented robotic system has successfully assembled various structures autonomously as shown in Figure 1, demonstrating the designed algorithms' effectiveness in autonomous on-site construction robotics applications.

show abstract

“…All three sign types utilize AprilTags [10] to convey information to the Duckiebots. The use of AprilTags in unison to images/text allows for a progression of functionality and programming skill sets.…”

Section: Introductionmentioning

confidence: 99%

Duckietown: An Innovative Way to Teach Autonomy

Tani

Paull

Zuber

et al. 2017

Educational Robotics in the Makers Era

View full text Add to dashboard Cite

Abstract. Teaching robotics is challenging because it is a multidisciplinary, rapidly evolving and experimental discipline that integrates cutting-edge hardware and software. This paper describes the course design and first implementation of Duckietown, a vehicle autonomy class that experiments with teaching innovations in addition to leveraging modern educational theory for improving student learning. We provide a robot to every student, thanks to a minimalist platform design, to maximize active learning; and introduce a role-play aspect to increase team spirit, by modeling the entire class as a fictional start-up (Duckietown Engineering Co.). The course formulation leverages backward design by formalizing intended learning outcomes (ILOs) enabling students to appreciate the challenges of: (a) heterogeneous disciplines converging in the design of a minimal self-driving car, (b) integrating subsystems to create complex system behaviors, and (c) allocating constrained computational resources. Students learn how to assemble, program, test and operate a self-driving car (Duckiebot) in a model urban environment (Duckietown), as well as how to implement and document new features in the system. Traditional course assessment tools are complemented by a full scale demonstration to the general public. The "duckie" theme was chosen to give a gender-neutral, friendly identity to the robots so as to improve student involvement and outreach possibilities. All of the teaching materials and code is released online in the hope that other institutions will adopt the platform and continue to evolve and improve it, so to keep pace with the fast evolution of the field.

show abstract

AprilTag: A robust and flexible visual fiducial system

Cited by 1,314 publications

References 21 publications

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

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

Towards Autonomous Robotic In-Situ Assembly on Unstructured Construction Sites Using Monocular Vision

Duckietown: An Innovative Way to Teach Autonomy

Contact Info

Product

Resources

About