iMoT: Inertial Motion Transformer for Inertial Navigation

TL;DR

iMoT introduces a Transformer-based inertial odometry approach that leverages novel modules like Progressive Series Decoupler and Adaptive Positional Encoding to improve accuracy and robustness in inertial navigation.

Contribution

The paper presents new Transformer components and mechanisms specifically designed for inertial odometry, enhancing motion event detection and cross-modal feature aggregation.

Findings

Outperforms state-of-the-art methods in inertial navigation accuracy.

Demonstrates robustness across various inertial datasets.

Improves motion event detection and uncertainty modeling.

Abstract

We propose iMoT, an innovative Transformer-based inertial odometry method that retrieves cross-modal information from motion and rotation modalities for accurate positional estimation. Unlike prior work, during the encoding of the motion context, we introduce Progressive Series Decoupler at the beginning of each encoder layer to stand out critical motion events inherent in acceleration and angular velocity signals. To better aggregate cross-modal interactions, we present Adaptive Positional Encoding, which dynamically modifies positional embeddings for temporal discrepancies between different modalities. During decoding, we introduce a small set of learnable query motion particles as priors to model motion uncertainties within velocity segments. Each query motion particle is intended to draw cross-modal features dedicated to a specific motion mode, all taken together allowing the model to refine its understanding of motion dynamics effectively. Lastly, we design a dynamic scoring mechanism to stabilize iMoT's optimization by considering all aligned motion particles at the final decoding step, ensuring robust and accurate velocity segment estimation. Extensive evaluations on various inertial datasets demonstrate that iMoT significantly outperforms state-of-the-art methods in delivering superior robustness and accuracy in trajectory reconstruction.