StarIO: 非線形運動に対応した軽量慣性オドメトリ
StarIO: A Lightweight Inertial Odometry for Nonlinear Motion
慣性センサデータを高次元の非線形特徴空間に射影し、注意機構とマルチスケールゲート畳み込みで複雑な運動パターンを捉える軽量な慣性オドメトリ手法を提案。
詳しい要約
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著者: Shanshan Zhang, Siyue Wang, Qi Zhang Liqin Wu, Tianshui Wen, Ziheng Zhou, Xuemin Hong, Lingxiang Zheng, Yu Yang
分類: cs.RO
原文アブストラクト
Inertial odometry (IO) directly estimates the position of a carrier from inertial sensor measurements and serves as a core technology for the widespread deployment of consumer grade localization systems. While existing IO methods can accurately reconstruct simple and near linear motion trajectories, they often fail to account for drift errors caused by complex motion patterns such as turning. This limitation significantly degrades localization accuracy and restricts the applicability of IO systems in real world scenarios. To address these challenges, we propose a lightweight IO framework. Specifically, inertial data is projected into a high dimensional implicit nonlinear feature space using the Star Operation method, enabling the extraction of complex motion features that are typically overlooked. We further introduce a collaborative attention mechanism that jointly models global motion dynamics across both channel and temporal dimensions. In addition, we design Multi Scale Gated Convolution Units to capture fine grained dynamic variations throughout the motion process, thereby enhancing the model's ability to learn rich and expressive motion representations. Extensive experiments demonstrate that our proposed method consistently outperforms SOTA baselines across six widely used inertial datasets. Compared to baseline models on the RoNIN dataset, it achieves reductions in ATE ranging from 2.26% to 65.78%, thereby establishing a new benchmark in the field.
