用增量位移估计提升惯性里程计精度，KAN模型更优
Delta-Position Estimation-Based IMU Odometry: A Comparison of MLP and Kolmogorov-Arnold Networks
- 以50毫秒窗口的增量位移代替绝对位置回归
- KAN模型误差仅MLP的44%，累计漂移9.61米
- 适合对长期定位精度有要求的无人机导航场景
本研究基于EuRoC MAV基准数据集中的原始IMU数据，探讨基于学习的惯性里程计问题。与易产生恒定误差的绝对位置回归不同，模型训练目标为固定50毫秒滑动窗口内的增量位移（Δp），通过数值积分重建完整轨迹。对比标准多层感知机（MLP）与采用可学习B样条激活函数的柯尔莫哥洛夫-阿诺德网络（KAN），KAN参数量仅为MLP的1/6.9（8,444对57,859），测试轨迹最终累积漂移却低44%（9.61米对17.23米），且在长期误差积累上更稳定，P_50和P_90累积漂移值更低。结果表明，基于可学习B样条的激活函数有助于降低惯性里程计中的误差累积。
原文摘要 · Abstract (English)
In this study, the learning-based inertial odometry problem is investigated using raw IMU measurements obtained from the EuRoC MAV benchmark dataset. Instead of absolute position regression-a formulation that may lead to large constant errors-the models are trained to estimate the incremental displacement (Δp) over a fixed 50 ms sliding window, and the full trajectory is reconstructed through numerical integration. A standard Multi-Layer Perceptron (MLP) is compared with a Kolmogorov-Arnold Network (KAN) equipped with learnable B-spline activations. Although KAN has 6.9 times fewer parameters than MLP (8,444 versus 57,859), it produces a 44% lower error in terms of final cumulative drift on the test trajectory (9.61 m versus 17.23 m). In addition, KAN exhibits more stable behavior in terms of long-term error accumulation, with lower P_50 and P_90 cumulative drift values. These findings indicate that learnable B-spline-based activations have the potential to reduce error accumulation in the inertial odometry problem.
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