陈明达, 应骏. 基于VI-DSO的改进单目视觉惯性里程计[J]. 微电子学与计算机, 2022, 39(8): 55-62. DOI: 10.19304/J.ISSN1000-7180.2021.1192
引用本文: 陈明达, 应骏. 基于VI-DSO的改进单目视觉惯性里程计[J]. 微电子学与计算机, 2022, 39(8): 55-62. DOI: 10.19304/J.ISSN1000-7180.2021.1192
CHEN Mingda, YING Jun. Improved monocular visual inertial odometer based on VI-DSO[J]. Microelectronics & Computer, 2022, 39(8): 55-62. DOI: 10.19304/J.ISSN1000-7180.2021.1192
Citation: CHEN Mingda, YING Jun. Improved monocular visual inertial odometer based on VI-DSO[J]. Microelectronics & Computer, 2022, 39(8): 55-62. DOI: 10.19304/J.ISSN1000-7180.2021.1192

基于VI-DSO的改进单目视觉惯性里程计

Improved monocular visual inertial odometer based on VI-DSO

  • 摘要: 针对目前纯直接法的视觉里程计缺乏尺度信息,优化位姿时容易陷入局部最优点,且单目视觉初始化时收敛速度较慢等问题,提出了一种改进的直接法单目视觉惯性里程计方案.基于目前效果较好的直接法视觉惯性里程计VI-DSO,提出修改方案.在初始化时,针对VI-DSO方案忽略IMU的初始化,采用后端统一优化的方式估计IMU偏置,导致尺度收敛慢,累积误差较大的问题,增加了IMU偏置及尺度的MAP快速预估,加快了初始化时尺度收敛的速度,同时也为后端优化提供了一个较精确的初始数据,减少累积误差;在深度估计中,改进了深度滤波方案,参考了SVO的滤波方法,利用高斯-均匀滤波器估计误匹配的概率,剔除错误的深度估计,融合正确的深度数据,提高定位精度;在边缘化过程中,完善了VI-DSO方案的边缘化策略,增加了对当前运动状态的判断,根据运动状态选择需要边缘化的帧,确保滑窗内有足够的视差.通过在EuRoc数据集中的测试结果表明,改进后的方案,初始化速度提升了33%,平均定位精度提高了34.5%.

     

    Abstract: Aiming at the problems that the visual odometry of the pure direct method lacks scale information, it is easy to fall into the local optimum when optimizing the pose, and the convergence speed is slow when monocular vision is initialized, an improved direct method monocular visual inertial odometry scheme is proposed. Based on the direct method visual inertial odometer VI-DSO with better effect at present, a modification scheme is proposed. In the initialization, the VI-DSO scheme ignores the initialization of the IMU, and uses the back-end unified optimization method to estimate the IMU bias, which leads to the slow convergence of the scale and the large cumulative error. The fast estimation of the IMU bias and the MAP of the scale accelerates the speed of the scale convergence during the initialization, and also provides a more accurate initial data for the back-end optimization to reduce the cumulative error. In the depth estimation, the depth filtering scheme is improved. Referring to the SVO filtering method, the Gaussian-uniform filter is used to estimate the probability of mismatching, eliminate the wrong depth estimation, and fuse the correct depth data to improve the positioning accuracy. In the process of marginalization, the marginalization strategy of the VI-DSO scheme is improved, and the judgment of the current motion state is increased. The frames that need to be marginalized are selected according to the motion state to ensure sufficient parallax in the sliding window. The test results in EuRoc dataset show that the improved scheme improves the initialization speed by 33 % and the average positioning accuracy by 34.5 %.

     

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