论文标题
通过在智能手机上使用惯性传感器的行人运动跟踪
Pedestrian Motion Tracking by Using Inertial Sensors on the Smartphone
论文作者
论文摘要
惯性测量单元（IMU）长期以来一直是稳定可靠的运动估算的梦想，尤其是在GPS强度限制的室内环境中。在本文中，我们提出了一种仅从手机收集的IMU信号序列的位置和方向估算的新颖方法。我们的主要观察结果是人类运动是单调的和周期性的。我们采用扩展的卡尔曼过滤器，并使用基于学习的方法动态更新过滤器的测量噪声。我们的行人运动跟踪系统旨在准确估算平面位置，速度，朝向方向，而无需限制手机的日常使用。该方法不仅在自收集的信号上进行了测试，而且还对公共RIDI数据集进行了准确的位置和速度估计，即，对于59秒的序列，绝对发送误差为1.28m。
Inertial Measurement Unit (IMU) has long been a dream for stable and reliable motion estimation, especially in indoor environments where GPS strength limits. In this paper, we propose a novel method for position and orientation estimation of a moving object only from a sequence of IMU signals collected from the phone. Our main observation is that human motion is monotonous and periodic. We adopt the Extended Kalman Filter and use the learning-based method to dynamically update the measurement noise of the filter. Our pedestrian motion tracking system intends to accurately estimate planar position, velocity, heading direction without restricting the phone's daily use. The method is not only tested on the self-collected signals, but also provides accurate position and velocity estimations on the public RIDI dataset, i.e., the absolute transmit error is 1.28m for a 59-second sequence.
