With the advancement of intelligent rail transit systems, traditional train positioning technologies have struggled to meet the demands of modern rail transportation networks. Deep learning-based intelligent visual positioning technology for trains has emerged as a critical solution to enhance operational efficiency and service quality in rail transit.
To address these challenges, ALG-TECH has developed an algorithm verification platform specifically tailored for rail transit scenarios. The platform integrates LiDAR, visual cameras, and inertial navigation modules to record and synchronize multi-modal sensor data. By ensuring real-time performance and precise data synchronization, this solution empowers the development of autonomous positioning systems for rail transit vehicles.
■ Multi-Sensor Data Acquisition: Supports the integration of various sensors for data acquisition, such as cameras, LiDAR, millimeter-wave radar, ultrasonic sensors, GPS, and IMU, to achieve comprehensive environmental perception and vehicle positioning.
■ High-Precision Real-Time Synchronization: Achieves high-precision time synchronization of sensor data, with the timestamp synchronization error between camera and LiDAR data being less than 1ms, radar timing accuracy less than 20μs, and exposure synchronization error of multiple cameras less than 10μs.
■ High Adaptability and Stability: Maintains stable operation in extreme environments, supports high-bandwidth data acquisition, and ensures the accuracy and integrity of data acquisition.
