IONext: Unlocking the Next Era of Inertial Odometry

• URL: http://arxiv.org/abs/2507.17089v1
• Date: Wed, 23 Jul 2025 00:09:36 GMT
• Title: IONext: Unlocking the Next Era of Inertial Odometry
• Authors: Shanshan Zhang, Siyue Wang, Tianshui Wen, Qi Zhang, Ziheng Zhou, Lingxiang Zheng, Yu Yang,
• Abstract summary: We present a new CNN-based inertial odometry backbone, named Next Era of Inertial Odometry (IONext)<n>IONext consistently outperforms state-of-the-art (SOTA) Transformer- and CNN-based methods.<n>For instance, on the RNIN dataset, IONext reduces the average ATE by 10% and the average RTE by 12% compared to the representative model iMOT.
• Score: 24.137981640306034
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Researchers have increasingly adopted Transformer-based models for inertial odometry. While Transformers excel at modeling long-range dependencies, their limited sensitivity to local, fine-grained motion variations and lack of inherent inductive biases often hinder localization accuracy and generalization. Recent studies have shown that incorporating large-kernel convolutions and Transformer-inspired architectural designs into CNN can effectively expand the receptive field, thereby improving global motion perception. Motivated by these insights, we propose a novel CNN-based module called the Dual-wing Adaptive Dynamic Mixer (DADM), which adaptively captures both global motion patterns and local, fine-grained motion features from dynamic inputs. This module dynamically generates selective weights based on the input, enabling efficient multi-scale feature aggregation. To further improve temporal modeling, we introduce the Spatio-Temporal Gating Unit (STGU), which selectively extracts representative and task-relevant motion features in the temporal domain. This unit addresses the limitations of temporal modeling observed in existing CNN approaches. Built upon DADM and STGU, we present a new CNN-based inertial odometry backbone, named Next Era of Inertial Odometry (IONext). Extensive experiments on six public datasets demonstrate that IONext consistently outperforms state-of-the-art (SOTA) Transformer- and CNN-based methods. For instance, on the RNIN dataset, IONext reduces the average ATE by 10% and the average RTE by 12% compared to the representative model iMOT.

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