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

2024-11-11

arXiv.org

AI Summary

Existing visual SLAM methods exhibit severe generalization deficits across diverse applications (e.g., XR, IoT, autonomous driving, UAVs, human pose tracking) and heterogeneous environments (indoor/outdoor, static/dynamic scenes, varying motion patterns), stemming from deep coupling among algorithm design, environmental characteristics, and platform motion dynamics. Method: We propose the first three-dimensional challenge taxonomy—“algorithm–environment–motion”—and systematically evaluate state-of-the-art methods (ORB-SLAM2/3, VINS-Fusion) on multi-source benchmarks (TUM, EuRoC, ARKitScenes, UAV-Human), quantifying performance via absolute trajectory error (ATE), relative pose error (RPE), and tracking loss. Contribution/Results: No method achieves robust cross-domain or intra-domain heterogeneous generalization. To address this, we introduce a principled co-optimization pathway comprising input representation disentanglement, intermediate information reuse, and output dynamic validation—establishing a reproducible benchmark and foundational design principles for universal visual localization.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: Localization, Mapping, and Navigation
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Advancements in tracking algorithms have empowered nascent applications across various domains, 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 robot navigation inside a building 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 algorithmic, environmental, and locomotion-related 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 different applications and scenarios within applications. 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.

Problem

• Evaluating tracking algorithm performance across diverse applications and scenarios
• Analyzing challenges in algorithmic, environmental, and locomotion-related tracking
• Improving tracking performance via data characterization and output evaluation

Innovation

• Evaluates tracking algorithms across diverse applications
• Categorizes challenges in algorithmic, environmental, locomotion aspects
• Proposes performance improvement via data and output analysis