ABSTRACT

Augmented reality (AR) has proven to be an invaluable interactive medium to reduce cognitive load by bridging the gap between the task-at-hand and relevant information by displaying information without disturbing the user's focus. AR is particularly useful in the manufacturing environment where a diverse set of tasks such as assembly and maintenance must be performed in the most cost-effective and efficient manner possible. While AR systems have seen immense research innovation in recent years, the current strategies utilised in AR for camera calibration, detection, tracking, camera position and orientation (pose) estimation, inverse rendering, procedure storage, virtual object creation, registration, and rendering are still mostly dominated by traditional non-AI approaches. This restricts their practicability to controlled environments with limited variations in the scene. Classical AR methods can be greatly improved through the incorporation of various AI strategies like deep learning, ontology, and expert systems for adapting to broader scene variations and user preferences. This research work provides a review of current AR strategies, critical appraisal for these strategies, and potential AI solutions for every component of the computational pipeline of AR systems. Given the review of current work in both fields, future research work directions are also outlined.

摘要

增强现实 (AR) 已被证明是一种宝贵的交互媒体，可通过在不干扰用户注意力的情况下显示信息来弥合手头任务和相关信息之间的差距，从而减少认知负荷。AR 在制造环境中特别有用，在这种环境中，必须以最具成本效益和最高效的方式执行各种任务，例如组装和维护。虽然 AR 系统近年来出现了巨大的研究创新，但 AR 中用于相机校准、检测、跟踪、相机位置和方向（姿势）估计、逆向渲染、过程存储、虚拟对象创建、注册和渲染的当前策略是仍然主要由传统的非人工智能方法主导。这限制了它们在场景变化有限的受控环境中的实用性。通过结合深度学习、本体和专家系统等各种人工智能策略，可以极大地改进经典 AR 方法，以适应更广泛的场景变化和用户偏好。这项研究工作回顾了当前的 AR 策略、对这些策略的批判性评估以及 AR 系统计算管道每个组件的潜在 AI 解决方案。鉴于对这两个领域当前工作的回顾，还概述了未来的研究工作方向。这项研究工作回顾了当前的 AR 策略、对这些策略的批判性评估以及 AR 系统计算管道每个组件的潜在 AI 解决方案。鉴于对这两个领域当前工作的回顾，还概述了未来的研究工作方向。这项研究工作回顾了当前的 AR 策略、对这些策略的批判性评估以及 AR 系统计算管道每个组件的潜在 AI 解决方案。鉴于对这两个领域当前工作的回顾，还概述了未来的研究工作方向。