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  • ISSN: 0577-6686

Journal of Mechanical Engineering ›› 2024, Vol. 60 ›› Issue (10): 3-21.doi: 10.3901/JME.2024.10.003

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Survey on Key Techniques for Visual and Inertial Based Odomety

ZHANG Yu1,2, TAN Zubing1,2, CAO Dongpu3, CHEN Long4   

  1. 1. School of Computer Science Engineering, Sun Yat-sen University, Guangzhou 510006;
    2. Institute of Unmanned Systems, Sun Yat-sen University, Guangzhou 510006;
    3. School of Vehicle and Mobility, Tsinghua University, Beijing 100084;
    4. Institute of Automation, Chinese Academy of Sciences, Beijing 100089
  • Received:2023-06-20 Revised:2024-01-15 Online:2024-05-20 Published:2024-07-24

Abstract: Environmental perception and state estimation is one of the key technologies of intelligent network coupling. Simultaneous location and mapping technology(SLAM), which is widely used in the field of intelligent network connected vehicles, aims to complete its own state estimation and environment modeling at the same time. Scholars in the SLAM field are committed to finding a balance between real-time and accuracy of the algorithm. Visual-inertial odometry(VIO), one of the instances of SLAM schema, is favored by most researchers because of its higher performance and lower price. VIO introduces IMU measurement on the basis of visual odometry(VO), which can not only improve the problem of scale drift, but also greatly alleviate the visual positioning failure caused by image overexposure and feature loss in the short term. As a perceptual measurement with good signal-to-noise ratio, the image can extract high-precision multi view geometric constraints, estimate inertial measurement unit(IMU) bias and noise, and eliminate the cumulative error. Thus, VIO not only improves the accuracy by combining redundant sensors, but also ensure the real-time performance of the system through sliding windows and state marginalization, which is a model taking into account both accuracy and operation efficiency. The standard definition and basic model of VIO system are introduced in detail, and its key modules, including initialization, visual information extraction and correlation, solution and optimization and calibration, are combed in detail and reviewed. The advantages and limitations of frontier work are analyzed in detail, and the commonly used visual inertia data sets are summarized. The existing problems and future development direction of vio are summarized and prospected.

Key words: state estimate, visual-inertial odometry, sensor fusion, simultaneous localization and mapping, visual-inertial dataset

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