Mulcol: AR Assembly Catalog (Marker-Based)

Quick Summary

• Role: Lead Developer (feliXart studios)
• Company/Client: Mulcol
• Project Period: 2017 (delivery)
• Platforms: iOS, Android
• Engine: Unreal Engine 4 (C++)
• AR Framework: ARToolKit (marker/NFT)
• Main Feature: Marker-based, step-by-step assembly guide

Timeline

• 2017: Development and delivery process.
• 2017-05-30: Page publication date (pubDate).

Industrial AR Assembly Catalog for Mulcol

The mobile AR assembly catalog I developed for Mulcol transformed printed catalogs into an interactive guide in the field. Users were able to monitor the 3D assembly steps in real time by scanning the markers in the catalog with the camera; This ensured the correct sequence, parts selection and installation standard.

Problems and Constraints

• Printed catalogs cannot provide fast and error-free guidance in the field.
• Need for stable tracking due to low light and different device cameras.
• High performance and low latency requirement on mobile devices.
• Optimizing large CAD-based models on mobile.

Solution Summary

ARToolKit combined marker/NFT-based tracking, 3D assembly animations and step-by-step guidance on Unreal Engine. Marker detection was made with the camera stream, the pose was calculated and the 3D content was displayed at the correct scale and location.

Architecture at a Glance

• Camera flow -> feature matching -> pose estimation -> Unreal transform -> 3D content.
• Detection on worker thread, scene update on game thread.
• Lens distortion compensation with calibration files.

Tracking & Pose Estimation (Marker/NFT)

• ARToolKit NFT pipeline implemented marker detection with FAST/ORB-like feature extraction.
• Homography was calculated with descriptor matches and 6DoF pose was produced.- Temporal smoothing and stabilization with fixed thresholds were applied to reduce jitter.

MobilePerformance

• Camera processing and marker detection were run in separate threads.
• The main loop is not blocked with thread-safe data transfer over the queue.
• Target performance: 60 FPS, measurement: Unreal profiling tools (device based testing).

Content Pipeline

• CAD models were optimized, LOD and texture compression were applied.
• Asynchronous loading and cleaning policy was used according to mobile memory limits.

UX: Step-by-step Assembly

• Steps were managed via DataTable, animation flow was established with Sequencer.
• Step numbers, part names and warnings were shown in the UMG interface.
• Forward/backward steps were controlled with touch and swipe gesture.

Marker-based vs Markerless (Brief Comparison)

• Marker-based provides high stability and low computational cost in controlled environments.
• Markerless (SLAM) is more flexible but can be unstable on low textured surfaces.
• Marker-based approach enables rapid onboarding in the field with a printed catalogue.
• Markerless approach makes content setup easier but requires more calibration.
• Marker-based offers safer FPS targeting on low-end devices.
• Markerless is more natural in terms of user experience, but battery consumption increases.

Impact/Results

• Reduction in assembly errors - 80% - period: [X], baseline: field assembly errors - source: field report/simulation KPI (client-reported).
• Setup time improvement - 40% - period: [X], baseline: manual catalog time - source: field reports (client-reported).
• Field operation standardization - qualitative improvement - source: field leader feedback (client-reported).- Mobile performance - 60 FPS target - measurement: device-based profile tests.

Key Trade-offs

• Marker-based accuracy vs markerless flexibility.
• Unreal Engine visual quality vs native performance cost.
• High accuracy vs battery consumption and device temperature.

FAQ

Why was marker-based AR chosen?

For stable tracking and fast onboarding in field conditions.

How does ARToolKit NFT work?

It matches feature points and creates pose with homography.

Why was Unreal Engine used?

For ease of 3D content management, rendering and cross-platform distribution.

How was mobile performance maintained?

With thread separation, LOD, texture compression and asynchronous loading.

How were the CAD models optimized?

With polygon reduction, LOD and light material settings.

Are offline scenarios supported?

Maintained basic guidance with cache and local content storage.

Project Imprint

• Client: Mulcol Fire Suppression Technologies
• Developer Company: feliXart studios
• Role: Lead Developer / Lead Software Developer
• Platform: iOS, Android
• Engine: Unreal Engine 4 -Core Language: C++
• AR Framework: ARToolKit (Marker-based Tracking)
• Completion Date: May 2017
• Page Date: 2017-05-30

Related Projects

• Mulcol: VR Firestop Assembly Simulation
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