FastSLAM algorithm based on weight optimal compensation extended kalman filter

In order to provide robots with truly autonomous capabilities, two essential technologies - localization and mapping - should be seen as one integral problem to be solved, which is called simultaneous localization and mapping (SLAM). The traditional FastSLAM2.0 algorithm is improved in this paper with using CEKF to compensate the linearization error resulted from EKF and using WOC to improve the resampling method. Thus, the importance proposal distribution will approach the true posterior probability density distribution and the particle impoverishment will be slowed. The accuracy and robustness of the improved algorithm are verified by simulations.