SiTAR: Situated Trajectory Analysis for In-the-Wild Pose Error Estimation

Scargill, Tim; Chen, Ying; Hu, Tianyi; Gorlatova, Maria

doi:10.1109/ismar59233.2023.00043

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2024

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Cited by 2 publications

References 60 publications

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SEESys: Online Pose Error Estimation System for Visual SLAM

Hu,

Scargill,

Yang

et al. 2024

Proceedings of the 22nd ACM Conference on Embedded Networked Sensor Systems

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SEESys: Online Pose Error Estimation System for Visual SLAM

Hu,

Scargill,

Yang

et al. 2024

Proceedings of the 22nd ACM Conference on Embedded Networked Sensor Systems

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Environment Texture Optimization for Augmented Reality

Scargill,

Janamsetty,

Fronk

et al. 2024

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Augmented reality (AR) platforms now support persistent, markerless experiences, in which virtual content appears in the same place relative to the real world, across multiple devices and sessions. However, optimizing environments for these experiences remains challenging; virtual content stability is determined by the performance of device pose tracking, which depends on recognizable environment features, but environment texture can impair human perception of virtual content. Low-contrast 'invisible textures' have recently been proposed as a solution, but may result in poor tracking performance when combined with dynamic device motion. Here, we examine the use of invisible textures in detail, starting with the first evaluation in a realistic AR scenario. We then consider scenarios with more dynamic device motion, and conduct extensive game engine-based experiments to develop a method for optimizing invisible textures. For texture optimization in real environments, we introduce MoMAR, the first system to analyze motion data from multiple AR users, which generates guidance using situated visualizations. We show that MoMAR can be deployed while maintaining an average frame rate > 59fps, for five different devices. We demonstrate the use of MoMAR in a realistic case study; our optimized environment texture allowed users to complete a task significantly faster (p=0.003) than a complex texture.

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SiTAR: Situated Trajectory Analysis for In-the-Wild Pose Error Estimation

Cited by 2 publications

References 60 publications

SEESys: Online Pose Error Estimation System for Visual SLAM

SEESys: Online Pose Error Estimation System for Visual SLAM

Environment Texture Optimization for Augmented Reality

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