EqNIO: Subequivariant Neural Inertial Odometry

Royina Karegoudra Jayanth, Yinshuang Xu, Daniel Gehrig, Ziyun Wang, Evangelos Chatzipantazis, Kostas Daniilidis

Keywords: equivariance, inertial odometry, subequivariance

TL;DR: Our framework robustly regresses an equivariant frame which improves the state-of-the-art in neural IO, when coupled with off-the-shelf filter-based or end-to-end models.

Abstract:

Neural networks that regress the displacement and associated covariance of an inertial
measurement unit (IMU) purely from its accelerometer and gyroscope measurements have
become key enablers to low-drift inertial odometry, but still ignore the physical roto-
reflective symmetries inherent in IMU data, thus hindering generalization. In this work, we
show that IMU data, displacements and covariances transform equivariantly, when rotated
around and reflected across planes parallel to gravity. We design a neural network that
equivariantly estimates a gravity-aligned frame from IMU data, leveraging tailored linear
and non-linear layers, and uses it to canonicalize the data. We train an off-the-shelf inertial
odometry network on this data and map its outputs back into the original frame, thus
obtaining equivariant covariances and displacements. To highlight its generality, we apply
the framework to both filter-based and end-to-end approaches and show better performance
on the TLIO, Aria, RIDI and OxIOD datasets than existing methods.

Submission Number: 10