用四元数无迹粒子滤波提升无人机在无卫星信号下的定位精度
Unscented Particle Filter for Visual-inertial Navigation using IMU and Landmark Measurements
- 基于四元数的无迹粒子滤波，捕捉非线性运动特性
- 实测数据表明定位误差显著低于地面真值
- 适合无人机等6自由度系统在无GPS环境使用
本文提出一种基于四元数的无迹粒子滤波方法（QUPF-VIN），专为六自由度（6 DoF）车辆设计，融合低成本惯性测量单元（IMU）与视觉传感器获取的地标观测数据。该方法采用离散形式实现，便于与机载惯性系统无缝集成。针对无卫星信号环境，基于搭载6轴IMU和双目相机的无人机真实数据集进行实验验证。数值结果表明，QUPF-VIN的轨迹跟踪精度优于地面真值；与传统卡尔曼滤波方法相比，性能显著提升。
原文摘要 · Abstract (English)
This paper introduces a geometric Quaternion-based Unscented Particle Filter for Visual-Inertial Navigation (QUPF-VIN) specifically designed for a vehicle operating with six degrees of freedom (6 DoF). The proposed QUPF-VIN technique is quaternion-based capturing the inherently nonlinear nature of true navigation kinematics. The filter fuses data from a low-cost inertial measurement unit (IMU) and landmark observations obtained via a vision sensor. The QUPF-VIN is implemented in discrete form to ensure seamless integration with onboard inertial sensing systems. Designed for robustness in GPS-denied environments, the proposed method has been validated through experiments with real-world dataset involving an unmanned aerial vehicle (UAV) equipped with a 6-axis IMU and a stereo camera, operating with 6 DoF. The numerical results demonstrate that the QUPF-VIN provides superior tracking accuracy compared to ground truth data. Additionally, a comparative analysis with a standard Kalman filter-based navigation technique further highlights the enhanced performance of the QUPF-VIN.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。