RIO: Rotation-equivariance supervised learning of robust inertial odometry

• URL: http://arxiv.org/abs/2111.11676v1
• Date: Tue, 23 Nov 2021 06:49:40 GMT
• Title: RIO: Rotation-equivariance supervised learning of robust inertial odometry
• Authors: Caifa Zhou, Xiya Cao, Dandan Zeng, Yongliang Wang
• Abstract summary: We introduce rotation-equivariance as a self-supervisor to train inertial odometry models. It reduces the reliance on massive amounts of labeled data for training a robust model. We propose adaptive Test-Time Training to enhance the generalizability of the inertial odometry to unseen data.
• Score: 8.943918790444272
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: This paper introduces rotation-equivariance as a self-supervisor to train inertial odometry models. We demonstrate that the self-supervised scheme provides a powerful supervisory signal at training phase as well as at inference stage. It reduces the reliance on massive amounts of labeled data for training a robust model and makes it possible to update the model using various unlabeled data. Further, we propose adaptive Test-Time Training (TTT) based on uncertainty estimations in order to enhance the generalizability of the inertial odometry to various unseen data. We show in experiments that the Rotation-equivariance-supervised Inertial Odometry (RIO) trained with 30% data achieves on par performance with a model trained with the whole database. Adaptive TTT improves models performance in all cases and makes more than 25% improvements under several scenarios.

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