Reliable navigation in GPS-denied environments remains a fundamental challenge in robotics, aerospace, and autonomous vehicle applications. This paper presents a Dual Quaternion-Based Unscented Kalman Filter (DQUKF) equipped with a Visual Inertial Odometry (VIO) algorithm for accurate state estimation enabling navigation in GPS denied locations. The proposed framework formulates the DQUKF in an error state manner, where the nominal pose is represented by a unit dual quaternion and the local pose error is represented by a 6-dimensional twistor parameterization used for sigma point generation, covariance propagation, and measurement correction. In parallel, the VIO algorithm tracks features across image frames, synchronizes measurements between the IMU and camera, and provides visual constraints that complement inertial propagation. Simulation results on the EuRoC MAV dataset show that the proposed DQUKF converges under high initialization uncertainty and achieves a position RMSE of 0.2584~m in the difficult flight sequence, outperforming the benchmark filters.

翻译：在GPS拒止环境中实现可靠导航仍是机器人、航空航天及自主车辆应用中的根本性挑战。本文提出了一种基于对偶四元数的无迹卡尔曼滤波（DQUKF），结合视觉惯性里程计（VIO）算法，以实现GPS拒止环境下的精确状态估计与导航。所提框架采用误差状态形式构建DQUKF，其中名义位姿由单位对偶四元数表示，局部位姿误差则通过6自由度扭量参数化描述，用于生成sigma点、传播协方差及修正量测。与此同时，VIO算法通过跨图像帧跟踪特征点、同步惯性测量单元（IMU）与相机量测，并提供视觉约束以补充惯性传播。在EuRoC MAV数据集上的仿真结果表明，所提DQUKF在高初始化不确定性条件下仍能收敛，在复杂飞行序列中达到0.2584米的位置均方根误差（RMSE），性能优于基准滤波器。