Piezoelectrics & Acoustooptics, Volume. 46, Issue 4, 474(2024)

Multi-State Constrained Kalman Filtering Based on Improved Keyframe Filtering

XIU Jinzhi1...2, FANG Zhen1,3, PENG Hui1,2, CHEN Yanping1,2, ZOU Mengqiang1,2, LIU Yu1,2, YANG Chenglin1,2, and WANG Sen12 |Show fewer author(s)
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  • 1[in Chinese]
  • 2[in Chinese]
  • 3[in Chinese]
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    The fusion algorithm based on multi-state constrained Kalman filtering solely uses a single frame image for pose estimation. If the initialization is incorrect, then it can cause severe divergence in visual pose estimation.Furthermore, each visual feature point in the system state vector can easily lead to computational burden to the system. Given the aforementioned problems, an improved keyframe selection algorithm is proposed, which uses multiple visual keyframes to constrain the same feature points for reducing visual measurement errors and improving positioning accuracy. Simultaneously, only the camera pose calculated from keyframes is integrated into the system state vector, which can effectively reduce system computation. The experiment shows that the improved algorithm enhances positioning accuracy and computational efficiency by 29.09% and 32.2%, respectively, when compared to EKF. Additionally, the proposed algorithm increases computational efficiency by 35.48% when compared to that of Orb-slam2.

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    XIU Jinzhi, FANG Zhen, PENG Hui, CHEN Yanping, ZOU Mengqiang, LIU Yu, YANG Chenglin, WANG Sen. Multi-State Constrained Kalman Filtering Based on Improved Keyframe Filtering[J]. Piezoelectrics & Acoustooptics, 2024, 46(4): 474

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    Paper Information

    Received: Dec. 25, 2023

    Accepted: --

    Published Online: Sep. 18, 2024

    The Author Email:

    DOI:10.11977/j.issn.1004-2474.2024.04.010

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