Mr. Jian Chen Profile

Jian Chen

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Proceedings Article | 4 January 2023 Poster + Presentation + Paper

Visual-inertial odometry based on tightly-coupled encoder

Zhangfang Hu, Zhenqian Guo, Yuan Luo, Jian Chen

Proceedings Volume 12317, 123170K (2023) https://doi.org/10.1117/12.2641165

KEYWORDS: Computer programming, Visualization, Cameras, Sensors, Robots, Motion estimation, Velocity measurements

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In order to solve the problem of inaccurate scale optimization of visual inertial odometer (VIO) algorithm under uniform motion, this paper presents a Visual-Inertial-Encoder Tightly-Coupled Odometry (VIETO) algorithm, and describes VIETO initialization as an optimal estimation problem in the sense of maximum-a-posteriori (MAP) estimation. Firstly, the pre-integration theory of encoder is introduced in this paper so that the scale and velocity information can be obtained by using the encoder to measure the pre-integration during the visual MAP estimation, which provides a good initial value for the optimal estimation of IMU parameters. Secondly, the encoder error term and random plane constraint are introduced into the visual inertia optimization framework to further constrain pose estimation. Finally, we apply VIETO to the monocular inertial ORB-SLAM3 system. By comparing the algorithm with other similar algorithms on the DS dataset, the results prove the effectiveness of the system.

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