Robust neural inertial navigation in the wild

Data-driven inertial navigation is the task of estimating of positions and orientations of a moving subject from a sequence of Inertial Measurement Unit (IMU) sensor measurements. Inertial navigation is a quintessential technology due to low cost, low energy consumption and low operating constraints of IMU sensor. However, sensor errors have forced research on inertial navigation to be limited to highly constrained use cases.We leverage on the power of machine learning and big data to loosen such constraints and estimate natural human motion in the wild. More concretely, we define our problem as estimation of relative horizontal positions and heading direction of a moving subject using the IMU sensor measurements from his phone. This research propose 1) a new benchmark containing more than 40 hours of IMU sensor data from 100 human subjects with ground-truth 3D trajectories under natural human motions; 2) novel neural inertial navigation architectures, making significant improvements for challenging motion cases; and 3) qualitative and quantitative evaluations of the competing methods over three inertial navigation benchmarks.We share the code and data to promote further research on our project website http://ronin.cs.sfu.ca