Evaluating Magic Leap 2 Controller Tracking for Sensor Tool Guidance in AR-Based Industrial Inspections

Rigorous evaluation of commercial Augmented Reality (AR) hardware is crucial, yet public benchmarks for tool tracking on modern Head-Mounted Displays (HMDs) are limited. This paper addresses this gap by systematically assessing the Magic Leap 2 (ML2) controller’s tracking performance. Using a robotic arm for repeatable motion (EN ISO 9283) and an optical tracking system as ground truth, our protocol evaluates static and dynamic performance under various conditions, including realistic paths from a hydrogen leak inspection use case. The results provide a quantitative baseline of the ML2 controller’s accuracy and repeatability and present a robust, transferable evaluation methodology. The findings provide a basis to assess the controller’s suitability for the inspection use case and similar industrial sensor-based AR guidance tasks.

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Published
2025-10-08
DOI
https://doi.org/10.1109/ismar-adjunct68609.2025.00089
Primary Topic
Industrial Vision Systems and Defect Detection
Type
article
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article

Evaluating Magic Leap 2 Controller Tracking for Sensor Tool Guidance in AR-Based Industrial Inspections

Thorsten Schüppstuhl, Christian Masuhr, Julian Koch
Industrial Vision Systems and Defect Detection
article

Evaluating Magic Leap 2 Controller Tracking for Sensor Tool Guidance in AR-Based Industrial Inspections

Thorsten Schüppstuhl, Christian Masuhr, Julian Koch
article en

Abstract

Rigorous evaluation of commercial Augmented Reality (AR) hardware is crucial, yet public benchmarks for tool tracking on modern Head-Mounted Displays (HMDs) are limited. This paper addresses this gap by systematically assessing the Magic Leap 2 (ML2) controller’s tracking performance. Using a robotic arm for repeatable motion (EN ISO 9283) and an optical tracking system as ground truth, our protocol evaluates static and dynamic performance under various conditions, including realistic paths from a hydrogen leak inspection use case. The results provide a quantitative baseline of the ML2 controller’s accuracy and repeatability and present a robust, transferable evaluation methodology. The findings provide a basis to assess the controller’s suitability for the inspection use case and similar industrial sensor-based AR guidance tasks.

Universität Hamburg (DE), Hamburg University of Technology (DE)
Openalex Percentile: Top 59%
Industrial Vision Systems and Defect Detection
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Evaluating Magic Leap 2 Controller Tracking for Sensor Tool Guidance in AR-Based Industrial Inspections — Thorsten Schüppstuhl, Christian Masuhr, et al. · (2025) | TGRS Research Map | TGRS