Work

Lost in Tracking Translation: A Comprehensive Analysis of Visual SLAM in Human-Centered XR and IoT Ecosystems

Year: 2025

Type: article

Abstract: Advances in tracking algorithms have enabled applications from steering autonomous vehicles to guiding robots to enhancing augmented reality experiences for users. However, these algorithms are application-specific and do not work across applications with different types of motion; even a tracking algorithm designed for a given application does not work in scenarios deviating from highly standard conditions. For example, a tracking algorithm designed for indoor robot navigation will not work for tracking the same robot in an outdoor environment. To demonstrate this problem, we evaluate the performance of the state-of-the-art tracking methods across various applications and scenarios. To inform our analysis, we first categorize environmental, locomotion-related, and algorithmic challenges faced by tracking algorithms. We quantitatively evaluate the performance using multiple tracking algorithms and representative datasets for a wide range of Internet of Things (IoT) and Extended Reality (XR) applications, including autonomous vehicles, drones, and humans. Our analysis shows that no tracking algorithm works across all applications and even in diverse scenarios within the same application. Ultimately, using the insights generated from our analysis, we discuss multiple approaches to improving the tracking performance using input data characterization, leveraging intermediate information, and output evaluation.

Source: ACM Transactions on Sensor Networks

DOI: 10.1145/3785140

Authors: Yasra Chandio, Khotso Selialia, Joseph Degol, Luis Garcia, Fatima Anwar

Institutions: University of Massachusetts Amherst, University of Utah

Language: English

Topic: Robotics and Sensor-Based Localization

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Funders: National Science Foundation, National Institutes of Health

Awards: Capturing Autobiographical memory formation in People moving Through real-world spaces Using synchronized wearables and intracranial Recordings of EEG, CAREER: Secure Timing Architecture for Untrusted Edge Systems, Collaborative Research: NCS: FO: Enhancing Episodic Memory through Real-world Integration of Brain Recording and Stimulation with Semantic Alignment of Human and IoT Perception