Physics-informed visual-inertial mamba for robust train localization in harsh conditions

Visual localization is a critical method for achieving reliable train localization, especially in Global Navigation Satellite System (GNSS)-denied environments. However, its effectiveness is severely hampered in harsh operational conditions, such as extreme weather and low-light tunnels, which induce significant visual degradation. To address this fundamental challenge, we propose PI-VIMamba, a novel physics-informed visual-inertial mamba framework. The innovation is centered around a tripartite technical system that integrates a backbone network, a detection module, and a depth estimation module. Firstly, an adaptive channel convolution module is proposed to dramatically enhance feature representation under visual degradation, leading to superior object detection accuracy and more precise depth estimation. Secondly, a kinematics-constrained loss function is introduced leveraging Inertial Measurement Unit (IMU) data. This physical constraint effectively suppresses monocular visual estimation drift, thereby ensuring high-precision, long-distance localization capability even in visually degraded environments. Finally, an improved mamba block is designed for refined general feature extraction, which substantially reduces the network's parameters and computational complexity while accelerating inference. Evaluated on real-world subway line data, PI-VIMamba demonstrably excels in harsh conditions, achieving state-of-the-art performance across key metrics, including detection accuracy, depth estimation precision, and computational efficiency, significantly outperforming existing mainstream models.