用双四元数优化视觉惯性导航，提升无GPS环境定位精度。
Dual Quaternion-Based Unscented Kalman Filter with Visual Inertial Odometry for Navigation in GPS-Denied Environments
- 采用双四元数与旋量参数化，改进滤波器状态表示
- 在EuRoC数据集上实现0.2584米位置均方根误差
- 适合高动态、初始化不确定的无人机导航场景
在无卫星信号环境下实现可靠导航仍是机器人、航空航天和自动驾驶领域的关键挑战。本文提出一种基于双四元数的无迹卡尔曼滤波器（DQUKF），结合视觉惯性里程计（VIO）算法，实现高精度状态估计，支持无GPS环境下的导航。该框架以误差状态形式构建，用单位双四元数表示参考姿态，6维旋量参数化表示局部姿态误差，用于生成采样点、协方差传播和测量修正。同时，VIO算法在图像帧间跟踪特征，同步惯性与视觉测量，提供互补的视觉约束。在EuRoC MAV数据集上的仿真结果表明，所提DQUKF在高初始不确定性下仍能收敛，在复杂飞行序列中达到0.2584米的位置均方根误差，优于基准滤波器。
原文摘要 · Abstract (English)
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.
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