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This paper sets a new foundation for data-driven inertial navigationresearch, where the task is the estimation of positions and orientations of amoving subject from a sequence of IMU sensor measurements. More concretely, thepaper presents 1) a new benchmark containing more than 40 hours of IMU sensordata from 100 human subjects with ground-truth 3D trajectories under naturalhuman motions; 2) novel neural inertial navigation architectures, makingsignificant improvements for challenging motion cases; and 3) qualitative andquantitative evaluations of the competing methods over three inertialnavigation benchmarks. We will share the code and data to promote furtherresearch.

RoNIN: Robust Neural Inertial Navigation in the Wild: Benchmark, Evaluations, and New Methods