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A Review of Visual-Inertial Simultaneous Localization and Mapping from Filtering-Based and Optimization-Based Perspectives

1,2, 1,2,*, 1,2 and 1,2
University School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221700, China
Jiangsu Collaborative Innovation Center of Intelligent Mining Equipment, Xuzhou 221700, China
Author to whom correspondence should be addressed.
Robotics 2018, 7(3), 45;
Received: 6 July 2018 / Revised: 4 August 2018 / Accepted: 10 August 2018 / Published: 15 August 2018
PDF [2049 KB, uploaded 20 August 2018]


Visual-inertial simultaneous localization and mapping (VI-SLAM) is popular research topic in robotics. Because of its advantages in terms of robustness, VI-SLAM enjoys wide applications in the field of localization and mapping, including in mobile robotics, self-driving cars, unmanned aerial vehicles, and autonomous underwater vehicles. This study provides a comprehensive survey on VI-SLAM. Following a short introduction, this study is the first to review VI-SLAM techniques from filtering-based and optimization-based perspectives. It summarizes state-of-the-art studies over the last 10 years based on the back-end approach, camera type, and sensor fusion type. Key VI-SLAM technologies are also introduced such as feature extraction and tracking, core theory, and loop closure. The performance of representative VI-SLAM methods and famous VI-SLAM datasets are also surveyed. Finally, this study contributes to the comparison of filtering-based and optimization-based methods through experiments. A comparative study of VI-SLAM methods helps understand the differences in their operating principles. Optimization-based methods achieve excellent localization accuracy and lower memory utilization, while filtering-based methods have advantages in terms of computing resources. Furthermore, this study proposes future development trends and research directions for VI-SLAM. It provides a detailed survey of VI-SLAM techniques and can serve as a brief guide to newcomers in the field of SLAM and experienced researchers looking for possible directions for future work. View Full-Text
Keywords: sensor fusion; robotics; SLAM; navigation; computer vision; localization sensor fusion; robotics; SLAM; navigation; computer vision; localization

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Chen, C.; Zhu, H.; Li, M.; You, S. A Review of Visual-Inertial Simultaneous Localization and Mapping from Filtering-Based and Optimization-Based Perspectives. Robotics 2018, 7, 45.

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