用类脑神经网络优化低成本惯导定位，提升精度与抗噪能力
Spiking Neural-Invariant Kalman Fusion for Accurate Localization Using Low-Cost IMUs
- 结合脉冲神经网络与不变扩展卡尔曼滤波，动态估计噪声参数
- 在KITTI和实测数据上定位误差显著低于现有方法
- 适合对噪声敏感的移动机器人实时定位场景
低成本惯性测量单元（IMU）因成本低、易集成，被广泛用于移动机器人定位。然而，其复杂的非线性时变噪声特性导致直接用于航位推算时定位精度严重下降。为此，我们提出一种受大脑启发的状态估计算法，将脉冲神经网络（SNN）与不变扩展卡尔曼滤波（InEKF）融合。SNN从受强随机噪声干扰的长序列IMU数据中提取运动特征，并通过代理梯度下降训练，实现对InEKF中协方差噪声参数的动态自适应。通过融合SNN输出与原始IMU测量值，所提方法显著提升了姿态估计的鲁棒性与准确性。在KITTI数据集及搭载低成本IMU的移动机器人真实数据上的大量实验表明，该方法在定位精度上优于当前最先进方法，且对传感器噪声具有强鲁棒性，展现出在实际移动机器人应用中的巨大潜力。
原文摘要 · Abstract (English)
Low-cost inertial measurement units (IMUs) are widely utilized in mobile robot localization due to their affordability and ease of integration. However, their complex, nonlinear, and time-varying noise characteristics often lead to significant degradation in localization accuracy when applied directly for dead reckoning. To overcome this limitation, we propose a novel brain-inspired state estimation framework that combines a spiking neural network (SNN) with an invariant extended Kalman filter (InEKF). The SNN is designed to extract motion-related features from long sequences of IMU data affected by substantial random noise and is trained via a surrogate gradient descent algorithm to enable dynamic adaptation of the covariance noise parameter within the InEKF. By fusing the SNN output with raw IMU measurements, the proposed method enhances the robustness and accuracy of pose estimation. Extensive experiments conducted on the KITTI dataset and real-world data collected using a mobile robot equipped with a low-cost IMU demonstrate that the proposed approach outperforms state-of-the-art methods in localization accuracy and exhibits strong robustness to sensor noise, highlighting its potential for real-world mobile robot applications.
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