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.

翻译：低成本惯性测量单元（IMU）因其经济性和易于集成的特点，被广泛应用于移动机器人定位。然而，其复杂、非线性且时变的噪声特性，在直接用于航位推算时往往导致定位精度显著下降。为克服这一局限，本文提出一种新颖的类脑状态估计框架，将脉冲神经网络（SNN）与不变扩展卡尔曼滤波器（InEKF）相结合。所设计的SNN能够从受强随机噪声影响的长序列IMU数据中提取运动相关特征，并通过代理梯度下降算法进行训练，以实现InEKF内部协方差噪声参数的动态自适应。通过将SNN输出与原始IMU测量值融合，所提方法增强了位姿估计的鲁棒性与准确性。在KITTI数据集及搭载低成本IMU的移动机器人采集的真实数据上进行的大量实验表明，该方法在定位精度上优于现有先进技术，并对传感器噪声表现出强鲁棒性，凸显了其在现实世界移动机器人应用中的潜力。