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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.

The visual-inertial-wheel odometry system significantly enhances the positioning accuracy of ground vehicles in global navigation satellite system (GNSS)-deprived environments. To fully exploit the fusion capabilities of vision, inertial measurement units (IMUs), and wheel odometers (referred to as odometry), precise calibration of their interrelated parameters is imperative. Conventional online calibration methods typically rely on a filtering framework, thereby limiting the potential improvement in online calibration accuracy. Therefore, we propose a self-calibrating online optimization approach based on the theory of preintegration for mounting misalignments and time offsets. Distinct from existing preintegration methods, a comprehensive IMU-odometer preintegration model is derived, considering the misalignments of position and attitude between the IMU and odometer, odometer scale factors, and IMU-odometer time offsets. Subsequently, to address camera-IMU time delays, visual factors with time offsets are designed. Then, the IMU-odometer preintegration and visual factors with time offsets are collectively incorporated into the graph-based optimization model, simultaneously optimizing spatial/temporal calibration parameters between sensors and the navigation state of the system. This overcomes the issue in existing online calibration methods that are influenced by the initial parameter values. Finally, both dataset and field test results indicate that our calibration method exhibits higher precision compared to other online calibration methods and offline tools. Our online calibration method achieves an error of approximately 0.013 m in the $X/Y$ axis position, about 0.30° error in the $Y/Z$ axis orientation, and a time delay error of approximately 0.44 ms between sensors. Furthermore, our approach serves as a versatile model easily applicable to optimization-based visual-inertial-wheel odometry frameworks.

Five-phase open-end winding permanent magnet synchronous motor (OEW-PMSM) fed by a dual inverter has gained increasing focus because of its advantages in voltage utilization, torque density, and reliability. In this article, an optimized modulation method with the sliding clamped strategy is proposed for the five-phase OEW-PMSM drive with a common dc bus. The conventional clamped strategies have a reduced switching sequence. However, the clamped inverter generates the zero-sequence voltage and third harmonic voltage, which would affect the performance of the motor. To solve this problem, the reference voltages in the proposed method are distributed by limiting the reference voltage of the clamped inverter on a line. And the reference voltage slides on this line bidirectionally. Therefore, the clamped inverter in the proposed method will not introduce additional harmonic components. For experimental verifications, the test results are presented with the resistance-inductance load and five-phase OEW-PMSM.