What question did this study set out to answer?

The aim is to improve precision in indoor trajectory estimation using pure IMUs by mitigating cumulative drift and complexity.

June 10, 2026Open Access

LKAN: A Kolmogorov–Arnold Network-Based Framework with Long-History Statistical Regularization for IMU Trajectory Estimation

Key Points

The aim is to improve precision in indoor trajectory estimation using pure IMUs by mitigating cumulative drift and complexity.
Proposes LKAN framework combining Kolmogorov–Arnold Network with Long-History Statistical Regularization.
Implements KANmer encoder integrating Multi-Head Self-Attention for capturing long-range dependencies.
Evaluates framework using three public datasets to validate performance and robustness.
Achieves an Absolute Trajectory Error of 2.04 m and Relative Trajectory Error of 2.72 m on the iIMU-TD dataset.
Reduces Absolute Trajectory Error by 33.8% and Relative Trajectory Error by 31.1% compared to ResT-IMU.
Demonstrates superior performance on the RoNIN dataset confirming robustness.

Abstract

High-precision indoor trajectory estimation using pure Inertial Measurement Units (IMUs) remains challenging due to severe cumulative drift and the complexity of modeling nonlinear dynamics. This paper proposes LKAN, a novel end-to-end framework that integrates the Kolmogorov–Arnold Network (KAN) with Long-History Statistical Regularization (LHSR). We design the KANmer encoder, which fuses Multi-Head Self-Attention with KAN to explicitly capture long-range temporal dependencies and intricate nonlinear features from IMU data. To enhance model robustness, a training-only Long-History Statistical Regularization mechanism is introduced; it effectively suppresses feature distribution drift by enforcing historical statistical consistency. Extensive evaluations on three public datasets demonstrate that LKAN significantly outperforms state-of-the-art methods in IMU-only pedestrian localization. Specifically, on the iIMU-TD dataset, LKAN achieves an Absolute Trajectory Error (ATE) of 2.04 m and a Relative Trajectory Error (RTE) of 2.72 m, representing a reduction of 33.8% and 31.1%, respectively, compared to the second-best ResT-IMU. Results on the RoNIN dataset further validate the superiority of LKAN. These findings confirm that LKAN effectively mitigates error accumulation, providing a reliable, high-precision solution for real-time IMU-based positioning in complex indoor environments.

Read Full Paperexternally

AI에게 질문

Bookmark

View Full Paper