Skip to content

AprilTag

  • Software
  • Perception
  • Library

A visual fiducial marker system for robotics: a small C library that detects tags in images and estimates their pose, with Python and other bindings.

Made by AprilRobotics.

Source on GitHub: github.com/AprilRobotics/apriltag (opens in a new tab). Facts read at commit b7c0ebe (opens in a new tab).

Licenses

Code
BSD-2-Clause (opens in a new tab)

Read by Robotheca from LICENSE.md (opens in a new tab) (BSD-2-Clause) at commit b7c0ebe (opens in a new tab), on 16 Oct 2026.

Commercial use
Yes

What it does

  • Detects AprilTag fiducial markers in images, with a detector more than twice as fast as AprilTag 2.
  • Improves detection rate on small tags and supports flexible tag layouts, including circular and hole-containing designs.
  • Provides pose estimation of a detected tag relative to the camera, given tag size and camera intrinsics.
  • Offers a C library with minimal dependencies, plus Python usage and wrappers for OpenCV, Matlab, Julia and WebAssembly.
  • Recommends the tagStandard41h12 family for most applications, with other families for more tags, circular objects or ArUco support.
  • Includes helper functions for deep-copying detector configurations and detection results for multi-threaded use.

Written by Robotheca from the project's README (opens in a new tab) at commit b7c0ebe.

Getting started

Difficulty
Intermediate
Hardware it needs, as the project states it
Officially only Linux is supported, though installation on Windows has worked for some users. Needs CMake to build; no external dependencies are required.

Maintenance

Status
Active: a commit in the last 90 days
Newest commit
7 Aug 2026 on master
Latest release
v3.4.5 (29 Aug 2025)

Checked on GitHub on 16 Oct 2026.

Robots on Robotheca that use it

No robot on Robotheca is linked to it yet. Links come from a robot's robot.yaml, from its dependency files read as text, or from Robotheca's editors.

Robotheca lists software as a reference: nothing is hosted or run here, and every fact links to where it was read.