Combined LiDAR/GPS/SINS navigation algorithm based on improved extended Kalman filtering

With the rapid development and wide application of UAVs, UAV navigation safety has become a crucial issue, which is directly related to the reliability and safety of the navigation system, however, in the conventional navigation system, it not only faces the defective positioning effect of a single sensor and the instability of the positioning system, but also restricts the effect of the perception algorithms in the actual flight due to the strained resources of the on-board processor. Aiming at the above problems, an improved extended Kalman filtering algorithm is designed with a three-filter parallel filtering mechanism, which assigns different weights to the output values of each filtering algorithm according to their measurement residuals, and then a weighted average of the three filtering algorithms' outputs finally outputs the optimal estimation value, so as to realize the effective fusion and utilization of the sampling results, and at the same time, it can obtain a more accurate position estimation, which improves the Positioning accuracy.