AprilTag: A robust and flexible visual fiducial system

By Edwin Olson. 2011

1,879 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology, 3D Vision & Pose

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Jitendra Malik
3. Sergey Levine
4. Vladlen Koltun
5. Xiaogang Wang
6. Li Fei-Fei
7. Dacheng Tao
8. Trevor Darrell

In Media Technology

1. John Wang
2. Rafael Muñoz-Salinas
3. Rafael Medina Carnicer
4. Timm Haucke
5. Volker Steinhage
6. Hjalmar S. Kühl
7. Francisco José Romero-Ramírez
8. Dumitru Erhan

In 3D Vision & Pose

1. Hjalmar S. Kühl
2. Timm Haucke
3. Volker Steinhage
4. John Wang
5. Rafael Muñoz-Salinas
6. Rafael Medina Carnicer
7. Francisco José Romero-Ramírez
8. Luca Carlone

Citations in the corpus

• SOCRATES: Introducing Depth in Visual Wildlife Monitoring Using Stereo Vision (2022, methodology): “We perform both intrinsic and extrinsic calibration using Kalibr (Maye et al., 2013) with a grid of 4 × 3 AprilTags (Olson, 2011) mounted on a wooden board as the calibration target.”
• Domain Separation Networks (2016, methodology): “An example is the labeling of a dataset with the use of AprilTags [21], 2D barcodes that can be used to label the pose of an object, provided that a camera is calibrated and the physical dimensions of the barcode are known.”
• Realtime State Estimation with Tactile and Visual Sensing. Application to Planar Manipulation (2017, neutral): “The Apriltag pose is tracked at 30 Hz.”
• Speeded up detection of squared fiducial markers (2018, methodology): “When compared to other approaches such as Chilitags, AprilTags, and ArToolKit+, our method achieves even higher speedups.”
• Productivity of digital fabrication in construction: Cost and time analysis of a robotically built wall (2018, neutral): “, localization) is done via 275 attached AprilTags (Olson, 2011), which the IF recognizes through built-in cameras.”
• The Multivehicle Stereo Event Camera Dataset: An Event Camera Dataset for 3D Perception (2018, methodology): “The camera intrinsics and extrinsics are estimated using a grid of AprilTags [35] that is moved in front of the sensor rig and calibrated using Kalibr.”
• Learning models for following natural language directions in unknown environments (2015, methodology): “As scene understanding is not the focus of this work, we use AprilTag fiducials [19] placed in each region that denotes its label.”
• Language to Rewards for Robotic Skill Synthesis (2023, methodology): “To detect the surface of the table with proper orientation, we use an AprilTag [54].”
• Vision-based autonomous quadrotor landing on a moving platform (2017, background): “Nevertheless, our framework can easily generalize to a variety of tags, as for example April Tags [22], and to different detection algorithms.”
• Duckietown: An open, inexpensive and flexible platform for autonomy education and research (2017, methodology): “In turn, sign detections can be “simulated” either by using fiducials (AprilTags [1]) affixed to each sign, or it can be implemented using “real” object detection.”
• AprilTag 2: Efficient and robust fiducial detection (2016, methodology): “AprilTag [3] built upon the advances of ARTag, introducing a lexicode-based system for generating tags.”
• The EuRoC micro aerial vehicle datasets (2016, methodology): “The corresponding calibration datasets (containing checkerboard and AprilTag (Olson, 2011) grid sequences) are provided with the datasets.”
• Autonomous Landing of a Quadcopter on a High-Speed Ground Vehicle (2017, methodology): “Most notably, Ling [10] shows that it is possible to use low cost sensors combined with an AprilTag fiducial marker [11] to land on a small ground robot.”