研究目的
Achieving lane-level localization in GNSS-challenged environments by fusing lidar data and cellular pseudoranges.
研究成果
The proposed framework achieves lane-level localization accuracy without GNSS signals by fusing lidar data and cellular pseudoranges, reducing position RMSE by at least 60% compared to lidar odometry-only approaches. It is computationally efficient and suitable for real-time applications in urban environments.
研究不足
The framework assumes spatially-stationary cellular towers and requires initial GNSS availability for mapping mode. Performance may degrade in environments with limited line-of-sight to cellular towers or high multipath effects.
1:Experimental Design and Method Selection:
The methodology involves an extended Kalman filter (EKF) framework that operates in two modes: mapping mode when GNSS signals are available and SLAM mode when GNSS signals are unavailable. It uses lidar odometry measurements and cellular pseudoranges for simultaneous localization and mapping.
2:Sample Selection and Data Sources:
Simulation data from the KITTI data sets and experimental data collected with a vehicle equipped with a lidar sensor, GPS-aided INS, and a cellular LTE receiver.
3:List of Experimental Equipment and Materials:
Velodyne HDL-64E lidar sensor, GPS receiver, cellular antennas, National Instruments USRP-2954R, and integrated GPS-IMU system.
4:Experimental Procedures and Operational Workflow:
The vehicle collects lidar scans and pseudorange measurements; in mapping mode, it estimates cellular tower states using GNSS; in SLAM mode, it fuses lidar odometry and pseudoranges to localize and map simultaneously.
5:Data Analysis Methods:
Root mean-squared error (RMSE) analysis for position estimation, comparison with ICP-only and cellular-only methods, and covariance estimation for error propagation.
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