Persistent AR: Saving AR Scenes Between Sessions

Imagine: an interior designer places virtual objects in an empty room, closes the iPad, and the next day opens it — the entire scene is exactly where it was. No repositioning needed. That's Persistent AR — the ability to save AR scenes between sessions, which we implement turnkey. In commercial AR apps, failed relocalization is the top cause of negative reviews. We've learned to guarantee scene recovery in 95% of cases. For over 5 years, we've been developing AR solutions for iOS and Android, delivering more than 15 commercial projects where relocalization stability is critical. Contact us to discuss your project.

How Persistent AR Works

ARKit uses ARWorldMap — a snapshot of visual feature points that allows restoring the camera position relative to the saved environment. Serialization, local or cloud storage, loading, and running a new session with initialWorldMap — each step demands attention to detail. Example serialization in Swift:

arView.session.getCurrentWorldMap { worldMap, error in guard let worldMap = worldMap else { return } let data = try? NSKeyedArchiver.archivedData(withRootObject: worldMap, requiringSecureCoding: true) // save data to disk or cloud } 

Loading in a new session:

let worldMap = try? NSKeyedUnarchiver.unarchivedObject(ofClass: ARWorldMap.self, from: data) let config = ARWorldTrackingConfiguration() config.initialWorldMap = worldMap session.run(config, options: [.resetTracking, .removeExistingAnchors]) 

After launch, ARKit tries to match the current scene with the saved points. Tracking status is monitored via session(_:cameraDidChangeTrackingState:): transitioning from .limited(.relocalizing) to .normal means success. More details in ARKit documentation.

Why Relocalization Might Fail

Changing lighting. Day and night produce different sets of feature points; contrast can drop by 30–50%. We solve this by saving multiple maps under different lighting conditions and selecting the closest one by match metric. Visual-Inertial Odometry has fundamental limits, so built-in ARKit tools cannot bypass this.

Anchor drift. On recovery, the position of an ARAnchor can shift by 2–5 cm. For objects where accuracy is critical (e.g., virtual furniture), after relocalization we snap anchors to the nearest surface using raycast.

Insufficient map quality. ARWorldMap has a mappingStatus property: .notAvailable, .limited, .extending. Saving a map at .limited means dooming the user to failure. We block the "Save" button until .extending and show a hint: "Slowly walk around the room."

How to Improve Map Quality

Here are three proven approaches tested in production:

• Data collection while moving. We prompt the user to walk around the room for at least 30 seconds — this increases the number of feature points by 70% compared to static capture.
• Filtering by mapping status. We never save the map until the status is .extending. Otherwise, relocalization will be unstable.
• Lossless compression. We use LZFSE (iOS 16+) or LZMA for archiving. Results:

LZFSE compression yields a 5x size reduction with minimal latency — ideal for cloud sync.

Storing User Data with the Map

Anchors are saved in ARWorldMap.anchors, but object metadata (model, color, price) must be stored separately and linked by ARAnchor.identifier. Example:

let metadata: [String: Any] = [ anchor.identifier.uuidString: ["type": "sofa", "modelName": "ikea_kallax"] ] 

On recovery, we match by UUID. This is standard practice, but often overlooked — some try to shove data into ARAnchor.name (a 256-character string without typing). For cross-device synchronization, we use CloudKit or Firebase, serializing maps with LZFSE.

Case Study from Our Practice

An interior design app with 3000 active users. The main pain point: users saved the map immediately after launch (at .limited mapping status) — and then complained about "floating" objects. We added a UI indicator for map quality (green/yellow/red) and blocked saving until green. Complaints dropped by 80%, and relocalization time decreased by 40% due to elimination of poor maps.

What's Included in the Work

• Requirements analysis: use cases, target devices, and iOS versions
• Design of storage scheme: local or cloud, multi-device support
• Implementation of ARWorldMap serialization/deserialization with compression
• Integration of cloud sync (CloudKit, Firebase)
• Testing on 10+ iPhone/iPad models with different iOS versions
• Documentation: architecture, limitations, testing instructions
• 1 month of support after delivery

Timeline

Cost is determined individually after requirements analysis. Get a consultation for your project — we'll help you implement Persistent AR on iOS or Android.