基于相机状态方程多模增广的改进MSCKF算法

针对基于多状态约束卡尔曼滤波(MSCKF)的视觉-惯性里程计算法中相机状态方程增广过程的误差累积问题,提出了一种相机状态方程多模增广方法。该方法首先对视觉特征跟踪状态的稳定性进行判断,然后自动地选择基于视觉图像信息优化求解相机相对位姿参数或基于惯性传感器(IMU)状态递推结果联合相机外参初始化新图像帧相机位姿参数两种途径增广相机的状态方程,解决特征跟踪状态稳定情况下IMU误差的累积问题。实验部分通过EuRo C数据和实际应用数据对算法的性能进行了验证分析,结果表明,改进后的MSCKF算法能够在特征跟踪稳定状态下有效避免IMU误差的累积,进一步融合视觉与惯性系统之间的互补优势,提高载体的定位定向精度和稳定性。

An improved MSCKF algorithm based onmulti mode augmentation method for the camera state equation

Aiming at the error accumulation problem ofthe visual inertial odometry algorithm based on multi state constraint kalman filter (MSCKF) in the augmentation process of the camera state equation, a multi mode augmentation method of camera state equation is proposed. In this method, the stability of the visual feature tracking state is strictly judged firstly; then, two methods are automatically selected to augment the camera state equation, the first method optimally solves the relative pose parameters of the camera based on visual image information,another method is based on the recursion results of inertial measurement unit (IMU) state combining the camera IMU external parameters to initialize the camera pose parameters for new image frame. As a result, the error accumulation problem of IMU under the stable feature tracking state is solved. In the experiment part, the performance of the proposed algorithm is verified utilizing the EuRoC dataset and practical application dataset. The experiment results show that the improved MSCKF algorithm can effectively avoid the error accumulation of IMU under the stable feature tracking state,further fuse the complementary advantages of both visual and inertial systems,and improve the localization & orientation precision and stability of the carrier.