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Sensors 2017, 17(11), 2613;

Monocular Visual-Inertial SLAM: Continuous Preintegration and Reliable Initialization

National Key Laboratory of Science and Technology on Multi-Spectral Information Processing, School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China
Beijing Aerospace Automatic Control Institute, Beijing 100854, China
Author to whom correspondence should be addressed.
Received: 5 September 2017 / Revised: 3 November 2017 / Accepted: 6 November 2017 / Published: 14 November 2017
(This article belongs to the Special Issue Imaging Depth Sensors—Sensors, Algorithms and Applications)
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In this paper, we propose a new visual-inertial Simultaneous Localization and Mapping (SLAM) algorithm. With the tightly coupled sensor fusion of a global shutter monocular camera and a low-cost Inertial Measurement Unit (IMU), this algorithm is able to achieve robust and real-time estimates of the sensor poses in unknown environment. To address the real-time visual-inertial fusion problem, we present a parallel framework with a novel IMU initialization method. Our algorithm also benefits from the novel IMU factor, the continuous preintegration method, the vision factor of directional error, the separability trick and the robust initialization criterion which can efficiently output reliable estimates in real-time on modern Central Processing Unit (CPU). Tremendous experiments also validate the proposed algorithm and prove it is comparable to the state-of-art method. View Full-Text
Keywords: sensor fusion; SLAM; computer vision; inertial navigation; tightly coupled sensor fusion; SLAM; computer vision; inertial navigation; tightly coupled

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Liu, Y.; Chen, Z.; Zheng, W.; Wang, H.; Liu, J. Monocular Visual-Inertial SLAM: Continuous Preintegration and Reliable Initialization. Sensors 2017, 17, 2613.

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