Visual Landmark-Aided LiDA-Inertial Odometry for Rail Vehicle

The precise localization of rail vehicles is crucial for their secure operation. In this article, we present a visual landmark-assisted light detection and ranging (LiDAR) and inertial fusion localization scheme, which adeptly addressing the key issues of absent global navigation satellite system (GNSS) signal and loopback-free areas encountered in railroad. To obtain a reliable visual landmark, we design a novel easy-to-deploy trackside kilometer post. Advanced deep learning-based programs are employed for its identification and character detection. View cone and deskewed point cloud plane are combined to achieve accurate positioning of the kilometer post's vertices. Furthermore, we construct a positioning framework based on fast direct LiDAR-inertial odometry (FAST-LIO2) that adapts to the train operating environment. This framework employs an error-state extended kalman filter (ESKF) to fuse LiDAR and inertial data as a frontend, constructs a factor graph for optimizing the multimodal information to obtain the best position estimation at the backend. It is worth noting that we leverage kilometer post plane factor and virtual vertex factor to carry out trajectory correction. Finally, our practical experiments demonstrate the excellent performance of the proposed method, achieving meter-level localization errors over long sequence of 8 km. Kilometer post identification and digit extraction excel in a variety of complex situations. Thanks to the auxiliary role of the kilometer post, it still provides stable and accurate position estimation even in severely degraded tunnel scenarios. Also, it can meet the real-time localization requirements of trains.