Abstract
This paper proposes a visual-inertial sensor fusion method to perform fast, robust and accurate pose estimation. The fusion framework includes two major modules: orientation fusion and position fusion. The orientation fusion is performed via the combination of vision and IMU (Inertial Measurement Unit) measurement. The position fusion is implemented via the combination of visual position measurement and accelerometer measurement using a proposed adaptive complementary filter. The proposed framework is robust to visual sensor failures from a poor illumination, occlusion or over-fast motion and is efficient in computation due to the adoption of complementary filters. Another important advantage is the error-reducing feature: the direction of the optical axis can be automatically compensated by Madgwick filter with inclination taken as magnetic distorsion. The performance is evaluated with a dual-arm manipulator. The results show a better pose estimation than visual sensor alone in terms of accuracy and robustness to vision failures.
Supported by the Key R&D Program of Zhejiang Province (2020C01026).
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Tong, K., Li, Y., Gu, N., Li, Q., Ren, T. (2021). A Novel Approach for Robust and Effective Pose Estimation via Visual-Inertial Fusion. In: Qiu, M. (eds) Smart Computing and Communication. SmartCom 2020. Lecture Notes in Computer Science(), vol 12608. Springer, Cham. https://doi.org/10.1007/978-3-030-74717-6_28
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