Master’s Thesis: Leveraging iPhone ARKit Pose Estimation for Visual SLAM and Scene Understanding

23.03.2026, Abschlussarbeiten, Bachelor- und Masterarbeiten

Accurate camera pose estimation is a fundamental component of many computer vision and augmented reality applications, including visual SLAM (Simultaneous Localization and Mapping), 3D reconstruction, and spatial understanding. Traditionally, pose estimation relies on classical Visual SLAM pipelines that use feature detection, tracking, and bundle adjustment to estimate camera motion and scene structure.

Recent iPhones provide a powerful inbuilt pose estimation system through ARKit, which internally combines camera data, IMU measurements, and visual feature tracking to deliver robust real-time device pose estimates. Preliminary experiments indicate that ARKit’s pose estimation performs surprisingly well, even in challenging environments. Furthermore, ARKit exposes information such as feature points, camera transforms, and world mapping status, suggesting that a classical VSLAM-inspired approach is used internally.

This research aims to investigate how Apple’s ARKit pose estimation system can be used, analyzed, and potentially extended for computer vision and robotics-related applications. The work will evaluate ARKit’s accuracy, robustness, and suitability as a replacement or complement to traditional Visual SLAM pipelines.

Kontakt: tim.schreiter@tum.de

Mehr Information

https://www.ce.cit.tum.de/pins/open-positions/student-positions/