Visual–Inertial Navigation Systems (VINS) plays an important role in many navigation applications. In order to improve the performance of VINS, a new visual/inertial integrated navigation method, named Sliding-Window Factor Graph optimised algorithm with Dynamic prior information (DSWFG), is proposed. To bound computational complexity, the algorithm limits the scale of data operations through sliding windows, and constructs the states to be optimised in the window with factor graph; at the same time, the prior information for sliding windows is set dynamically to maintain interframe constraints and ensure the accuracy of the state estimation after optimisation. First, the dynamic model of vehicle and the observation equation of VINS are introduced. Next, as a contrast, an Invariant Extended Kalman Filter (InEKF) is constructed. Then, the DSWFG algorithm is described in detail. Finally, based on the test data, the comparison experiments of Extended Kalman Filter (EKF), InEKF and DSWFG algorithms in different motion scenes are presented. The results show that the new method can achieve superior accuracy and stability in almost all motion scenes.

中文翻译：

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一种基于滑窗因子图优化的视觉/惯性组合导航新算法

视觉惯性导航系统 (VINS) 在许多导航应用中发挥着重要作用。为了提高VINS的性能，提出了一种新的视觉/惯性组合导航方法，称为动态先验信息的滑动窗口因子图优化算法（DSWFG）。为了约束计算复杂度，该算法通过滑动窗口限制数据操作的规模，并在窗口中用因子图构造待优化的状态；同时，动态设置滑动窗口的先验信息，保持帧间约束，保证优化后状态估计的准确性。首先介绍了车辆的动力学模型和VINS的观测方程。接下来，作为对比，构造不变扩展卡尔曼滤波器（InEKF）。然后，详细描述了 DSWFG 算法。最后，基于测试数据，给出了扩展卡尔曼滤波器（EKF）、InEKF和DSWFG算法在不同运动场景下的对比实验。结果表明，新方法几乎可以在所有运动场景中实现卓越的准确性和稳定性。