Pedestrian Motion Tracking by Using Inertial Sensors on the Smartphone
This addresses indoor localization for pedestrians, but it is incremental as it builds on existing filtering techniques with minor enhancements.
The paper tackles pedestrian motion tracking using smartphone inertial sensors by proposing a method that combines Extended Kalman Filter with learning-based noise updates, achieving an absolute transmit error of 1.28m over a 59-second sequence on a public dataset.
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.