“Tracking type” in AR refers to how the glasses determine the user’s motion and the real-world coordinate system, so that virtual content stays locked to the correct positions.

 

 

 

1) SLAM tracking (Simultaneous Localization and Mapping)

Idea: The glasses create and update a map of the environment while estimating their own position within it.

How it works (high level)

• Use cameras (and often IMU) to detect visual features.
• Estimate the device pose (position + orientation) relative to previously seen features.
• Build/update a 3D map (point cloud, planes, sometimes meshes).
• Use the map to keep anchors stable.

Pros

• Works in many environments (especially those with texture/visual features).
• Doesn’t require pre-built markers or special setup.
• Can support larger spaces because it continuously builds the world model.

Cons/failure modes

• Can drift over time (though modern SLAM reduces this a lot).
• Performance drops in low light, motion blur, or low-texture scenes (blank walls).
• Changes in the environment can reduce stability.

2) Marker-based tracking

Idea: The system recognizes known “targets” (markers) in the scene and uses them to compute pose.

Common marker types:

• Visual fiducials: QR-like patterns, ArUco markers, AprilTags.
• Feature targets: printed images or special tracking cards.
• Visual anchor images: sometimes called image-based tracking (detecting reference pictures).
• UWB/Beacon-based (not visual, but “marker-like” anchors): device measures distance to known beacons.

How it works (high level)

• The camera detects the marker (or reference target).
• The glasses compute pose directly from the detected marker geometry.
• Virtual content is anchored to that marker’s known location.

Pros

• Often very stable and accurate as long as the marker is visible.
• Less computationally heavy than full SLAM in some setups.
• Great for repeatable tasks (training modules, industrial workstations).

Cons/failure modes

• Needs markers/targets present (or pre-installed environments).
• If the marker is occluded, moved, or out of view → tracking can fail or fall back.
• Less flexible for roaming/unknown environments unless you install lots of markers or use many reference targets.

3) When each is typically chosen

• SLAM: general-purpose AR, warehouses you move through, museums, outdoor/varied environments.
• Marker-based: factory assembly stations, labs, classrooms, “put this part here” workflows on fixed fixtures.

4) Hybrid approach (common in real products)

Many systems use both:

• Marker-based when targets are visible (stability/accuracy)
• SLAM when markers aren’t visible (coverage)

5) How to tell them apart in practice

• With SLAM, anchors generally “hold” as you move around, even without markers — until tracking starts to decline.
• With marker-based tracking, it works best when you can see the markers; occlusion or leaving the area often leads to noticeable tracking issues.