Review on SLAM algorithms for Augmented Reality

Abstract

Augmented Reality (AR) has gained significant attention in recent years as a technology that enhances the user’s perception and interaction with the real world by overlaying virtual objects. Simultaneous Localization and Mapping (SLAM) algorithm plays a crucial role in enabling AR applications by allowing the device to understand its position and orientation in the real world while mapping the environment. This paper first summarizes AR products and SLAM algorithms in recent years, and presents a comprehensive overview of SLAM algorithms including feature-based method, direct method, and deep learning-based method, highlighting their advantages and limitations. Then provides an in-depth exploration of classical SLAM algorithms for AR, with a focus on visual SLAM and visual-inertial SLAM. Lastly, sensor configuration, datasets, and performance evaluation for AR SLAM are also discussed. The review concludes with a summary of the current state of SLAM algorithms for AR and provides insights into future directions for research and development in this field. Overall, this review serves as a valuable resource for researchers and engineers who are interested in understanding the advancements and challenges in SLAM algorithms for AR.