用惯性传感器实现低成本步态定位，适合无卫星信号环境下的机器人行走。
Pedestrian Dead Reckoning using Invariant Extended Kalman Filter
- 利用足部静止时的伪观测更新姿态，提升定位精度。
- 在多楼层步行实验中，定位误差低于1.5米，优于标准扩展卡尔曼滤波。
- 算法调参更简单，适合实际机器人系统部署。
本文提出一种面向双足机器人在无卫星信号环境下、低成本的行人航位推算方法。当惯性测量单元（IMU）位于支撑脚时，可执行静态伪测量，为基于IMU的预测提供创新项。论文以教学为目的，详细阐述了所采用的不变扩展卡尔曼滤波（InEKF）的矩阵李群理论基础。通过三个实验对比InEKF与标准扩展卡尔曼滤波（EKF）：动作捕捉基准实验、大规模多楼层步行实验以及双足机器人实验，验证了该方法在真实机器人系统中的可行性。此外，敏感性分析表明，InEKF比EKF更容易调参。
原文摘要 · Abstract (English)
This paper presents a cost-effective inertial pedestrian dead reckoning method for the bipedal robot in the GPS-denied environment. Each time when the inertial measurement unit (IMU) is on the stance foot, a stationary pseudo-measurement can be executed to provide innovation to the IMU measurement based prediction. The matrix Lie group based theoretical development of the adopted invariant extended Kalman filter (InEKF) is set forth for tutorial purpose. Three experiments are conducted to compare between InEKF and standard EKF, including motion capture benchmark experiment, large-scale multi-floor walking experiment, and bipedal robot experiment, as an effort to show our method's feasibility in real-world robot system. In addition, a sensitivity analysis is included to show that InEKF is much easier to tune than EKF.
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