Tightly coupled SLAM/GNSS for land vehicle navigation

Simultaneous Localization and Mapping (SLAM) algorithm takes the advantages of online map building without any prior environment information and simultaneously location determining with the generated map. This paper proposes an innovative navigation algorithm, tightly coupling of SLAM and GNSS. If GNSS signals are available, the GNSS raw measurements are fused with SLAM measurements to correct the errors of the system's pose as well as reducing the uncertainty of the map. In the GNSS-denied environments, the system operates at the stand-alone SLAM to provide continuous navigation solutions. Considering the computational cost problem, Compressed Extended Kalman Filter (CEKF) is employed to the multi-sensor data fusion. The simulation of the proposed algorithm is implemented in the simulated large-scale environment. Results demonstrate that the proposed technique provides a high accuracy of trajectory tracking in complex environments, and improves greatly the performance of data association and loop-closure detection.