Eccentric Optimization of Multisensor for SLAM-Integrated Navigation

This article models, first, the eccentric installation of multiple sensors and inertial measurement unit (IMU), both dynamically and physically, based on the simultaneous localization and mapping (SLAM)-integrated navigation in the near-ground environment. Furthermore, the relationship between the estimated acceleration of other sensor locations under the eccentric motion and the actual measurement value of the accelerometer has been analyzed. Most SLAM-integrated navigation fusion algorithms incorporating IMU only account for the transformation between the sensor coordinate systems, but avoid the Coriolis, tangential, and centrifugal forces caused by the eccentric rotation and the sensor installed at different locations. This problem affects the accuracy of the sensor acceleration estimated at different locations and thus affects the accuracy of the SLAM system preintegration and the overall positioning accuracy of SLAM. Second, the eccentric correction optimization and preintegration optimization algorithm have been implemented. Finally, a number of simulation experiments have been carried out to verify the optimal effect of the algorithm. The experimental results show that the positioning accuracy of the overall SLAM navigation algorithm can be effectively improved by eccentric correction optimization, and the root mean squared error (RMSE) of the optimized SLAM navigation algorithms has been reduced in varying degrees in different paths and algorithms. This study will help optimize the positioning accuracy of the SLAM- and IMU-integrated navigation scheme in the future.