Abstract
Under the large noise, the system's observability is weak, which leads to the instability of the filtering algorithm and the slow convergence speed. The algorithm of simultaneous localization and mapping based on the square-root Cubature Kalman filter (SRCKF) with Spherical Simplex(SS) was proposed in this paper. It not only abandoned the intercept term of traditional filtering algorithm, but also avoided the large amount of computation in sigma point algorithm. The new algorithm was used in the navigation, and the simulation experiment results and data showed that the number of sampling points and the amount of calculation were reduced because of the hypersphere distribution sampling. At the same time, the square root decomposition guaranteed the non-negative of the matrix, and the algorithm had s a good stability and a certain navigation precision.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call