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DTSTART;TZID=America/New_York:20241017T110000
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DTSTAMP:20260603T030535
CREATED:20241010T181407Z
LAST-MODIFIED:20241010T181407Z
UID:12351-1729162800-1729166400@seasevents.nmsdev7.com
SUMMARY:IDEAS Seminar: "Equivariant Neural Inertial Odometry"
DESCRIPTION:Abstract:  \nIn this talk\, we introduce a new class of problems related to integrating inertial measurements obtained from an IMU that play a significant role in navigation combined with visual data. While there have been tremendous technological advances in the precision of instrumentation\, integrating acceleration and angular velocity still suffers from drift in the displacement estimates. Neural networks have come to the rescue in estimating displacement and the associated uncertainty covariance. However\, such networks do not consider the physical roto-reflective symmetries inherent in IMU data\, leading to the need to memorize the same priors for every possible motion direction\, which hinders generalization. In this work\, we characterize these symmetries and show that the IMU data and the resulting displacement and covariance transform equivariantly when rotated around the gravity vector and reflected with respect to arbitrary\nplanes parallel to gravity. We propose a network for predicting an equivariant gravity-aligned frame from equivariant vectors and invariant scalars derived from IMU data\, leveraging expressive linear and non-linear layers tailored to commute with the underlying symmetry transformation. Such a canonical frame can precede existing architectures that are end-to-end or filter-based. We will include an introduction to the inertial filtering problem and we will present\nresults in real-world datasets. \nZoom link: https://caltech.zoom.us/j/88141815847?pwd=ayPBbwoDE91IWysv1P4Oxp6zbamrSQ.1
URL:https://seasevents.nmsdev7.com/event/ideas-seminar-equivariant-neural-inertial-odometry/
LOCATION:Room 401B\, 3401 Walnut\, 3401 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar,Colloquium
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