Abstract
An integrated autonomous relative navigation method based on vision and IMU data fusion was proposed in this paper, which can improve the position accuracy effectively and has strong adaptability to environmental changes. Firstly, IMU pre-integration formula based on Runge Kutta method was derived, which can improve the pre-integration position accuracy and reduce the accumulated error effectively. Secondly, an inverse depth estimation method based on the mixed probability model was proposed during the system initialization process, which can improve the accuracy of camera depth estimation and provide better initial conditions for back-end optimization. Thirdly, a sliding window filtering method based on the probability graph was proposed, which can avoid repeated calculations and improve the sliding window filtering efficiency. Forthly, combined with the advantages of the direct method and the feature point method, a mixed re-projection optimization method was proposed, which can expand the application scope of the method and improve the optimization accuracy effectively. Finally, in the closed-loop optimization, a closed-loop optimization method based on similar transformation is proposed to eliminate the accumulated error. In order to verify the environmental adaptability of the method and the impact of closed-loop detection on the relative navigation system, indoor and outdoor experiments were carried out with a hand-held camera and an IMU. EuRoC dataset was used in the experiments and the proposed method was compared with some classical methods. The experimental results showed that this method has high accuracy and robustness.
Highlights
With the continuous development of driverless technology, in order to adapt to various complex environmental conditions and resist random interferences, the autonomous navigation is more and more valued by many researchers
In the back-end optimization, the sliding window filtering principle based on the probability graph was proposed to improve the filtering efficiency greatly, and the back-end optimization was carried out with the re-projection error function calculated with the fusion of the direct method and the feature point method to reduce the overall error of the system
During the fusion locating process with vision and IMU data, the pre-integration of IMU is very important in the whole optimization process, which can avoid the problem of repeated calculations effectively in the optimization process
Summary
With the continuous development of driverless technology, in order to adapt to various complex environmental conditions and resist random interferences, the autonomous navigation is more and more valued by many researchers. In the back-end optimization, the sliding window filtering principle based on the probability graph was proposed to improve the filtering efficiency greatly, and the back-end optimization was carried out with the re-projection error function calculated with the fusion of the direct method and the feature point method to reduce the overall error of the system. This paper is organized as below: IMU pre-integration based on Runge Kutta method and system initialization will be introduced in Section II - Front-end Processing; the sliding window filtering principle based on the probability graph and the re-projection error optimization method mixed with the direct method and the feature point method will be introduced in Section III - Back-end Optimization; the closed-loop.
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