Figure 1: Smartphones with motion sensors are ubiquitous in modern life. This paper proposes a novel data-driven approach for inertial navigation, which uses Inertial Measurement Unit (IMU) in every smartphone to estimate trajectories of natural human motions. IMU is energy-efficient and works anytime anywhere even for smartphones in your pockets or bags. This paper proposes a novel data-driven approach for inertial navigation, which learns to estimate trajectories of natural human motions just from an inertial measurement unit (IMU) in every smartphone. The key observation is that human motions are repetitive and consist of a few major modes (e.g., standing, walking, or turning). Our algorithm regresses a velocity vector from the history of linear accelerations and angular velocities, then corrects low-frequency bias in the linear accelerations, which are integrated twice to estimate positions. We have acquired training data with ground-truth motions across multiple human subjects and multiple phone placements (e.g., in a bag or a hand). The qualitatively and quantitatively evaluations have demonstrated that our algorithm has surprisingly shown comparable results to full Visual Inertial navigation. To our knowledge, this paper is the first to integrate sophisticated machine learning techniques with inertial navigation, potentially opening up a new line of research in the domain of data-driven inertial navigation. We will publicly share our code and data to facilitate further research.
Data-driven priors: Robust IMU double integration (RIDI) was the first data driven Inertial navigation method REF .
RIDI: Robust IMU Double Integration
ArXiv
ArXiv
Computer Science