论文标题
不变的延长卡尔曼过滤以进行人体运动估计，并使用不完美的传感器放置
Invariant Extended Kalman Filtering for Human Motion Estimation with Imperfect Sensor Placement
论文作者
论文摘要
本文介绍了一种新的不变扩展的卡尔曼滤波器设计，该设计即使在存在传感器未对准和初始状态估计误差的情况下，也会产生实时状态估计和快速误差收敛，以估计人体运动。过滤器融合了连接到人体（例如骨盆或胸部）的惯性测量单元（IMU）返回的数据，并虚拟测量零姿态 - 脚步速度（即腿部音量法）。所提出的过滤器的主要新颖性在于，其过程模型符合组仿射特性，而滤波器通过将其随机过程模型制定为布朗尼运动并将误差纳入腿部音量计，从而明确解决了IMU放置误差。尽管测量模型是不完美的（即，它没有不变的观察形式），因此其线性化依赖于状态估计，但实验结果表明，即使在显着的IMU放置不正确的估算和初始估计错误和初始估计错误下，在蹲下运动过程中，提议的过滤器（在0.2秒内）的快速收敛（在0.2秒内）。
This paper introduces a new invariant extended Kalman filter design that produces real-time state estimates and rapid error convergence for the estimation of the human body movement even in the presence of sensor misalignment and initial state estimation errors. The filter fuses the data returned by an inertial measurement unit (IMU) attached to the body (e.g., pelvis or chest) and a virtual measurement of zero stance-foot velocity (i.e., leg odometry). The key novelty of the proposed filter lies in that its process model meets the group affine property while the filter explicitly addresses the IMU placement error by formulating its stochastic process model as Brownian motions and incorporating the error in the leg odometry. Although the measurement model is imperfect (i.e., it does not possess an invariant observation form) and thus its linearization relies on the state estimate, experimental results demonstrate fast convergence of the proposed filter (within 0.2 seconds) during squatting motions even under significant IMU placement inaccuracy and initial estimation errors.
