IONet: Learning to Cure the Curse of Drift in Inertial Odometry
By Changhao Chen, Chris Xiaoxuan Lu, Andrew Markham, Agathoniki Trigoni. AAAI Conference on Artificial Intelligence, 2018
383 citations recorded by the source corpus.
Inertial sensors play a pivotal role in indoor localization, which in turn lays the foundation for pervasive personal applications. However, low-cost inertial sensors, as commonly found in smartphones, are plagued by bias and noise, which leads to unbounded growth in error when accelerations are double integrated to obtain displacement. Small errors in state estimation propagate to make odometry virtually unusable in a matter of seconds. We propose to break the cycle of continuous integration, and instead segment inertial data into independent windows. The challenge becomes estimating the latent states of each window, such as velocity and orientation, as these are not directly observable from sensor data. We demonstrate how to formulate this as an optimization problem, and show how deep recurrent neural networks can yield highly accurate trajectories, outperforming state-of-the-art shallow techniques, on a wide range of tests and attachments. In particular, we demonstrate that IONet can generalize to estimate odometry for non-periodic motion, such as a shopping trolley or baby-stroller, an extremely challenging task for existing techniques.
Subfields: Aerospace Engineering, Biomedical Engineering, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/69f7deeeb4a8b6e9a0eb8129b15055299a2a6c5e
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In Aerospace Engineering
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Citations in the corpus
- RoNIN: Robust Neural Inertial Navigation in the Wild: Benchmark, Evaluations, & New Methods (2019, background): “IONet is a neural network based approach, which regresses the velocity magnitude and the rate of motion-heading change without relying on external device orientation information [14].”
- TRADI: Tracking deep neural network weight distributions (2019, methodology): “With the advent of DNNs, researchers have tried integrating ideas from KF in DNN training: for SLAM using RNNs [8, 16], optimization [51], DNN fusion [36].”