A Robust and Reliable Positioning Method for Complex Environments Based on Quality-Controlled Multi-Sensor Fusion of GNSS, INS, and LiDAR

The multi-source fusion localization algorithm demonstrates advantages in achieving continuous localization. However, its reliability and robustness could not be guaranteed and still with some insufficiencies in complex environments, especially for severe occlusions and low-texture scenes in non-cooperative scenarios. In this paper, we propose a GNSS/INS/LiDAR multi-source fusion localization framework. To enhance the algorithm’s performance, availability of different sensors is evaluated quantitatively through GNSS/INS status detection, and LiDAR-data-feature repeatability quality control is implemented at the front end. Both the variability of the standard deviation of differences of features and the standard deviation of real-time features are designed as major considerations and proposed to characterize the repeatability of 3D point clouds of LiDAR. The prior probability of the sensor covariance within the factor graph improves the algorithm’s fusion weight adjustment capability. Finally, a GNSS/INS/LiDAR multi-sensor positioning test platform is developed, and experiments are conducted in sheltered and semi-sheltered environments, such as urban, tunnel, campus, and mountainous environments. The results show that, compared with state-of-the-art methods, the proposed algorithm exhibits superior adaptability, significantly enhancing both reliability and robustness in four different typical real, complex environments, and our algorithm improves the robust running time by 44% in terms of availability in large-scale urban tests. In addition, the algorithm demonstrates superior positioning accuracy compared with those of other methods, achieving a positioning accuracy (RMSE) of 0.18 and 0.21 m in large-scale, long-duration urban and mountainous settings, respectively.