A SLAM algorithm based on central difference particle filter

There are two serious drawbacks in FastSLAM (Simultaneous localization and mapping), which are the derivation of the Jacobian matrices and the linear approximations of nonlinear functions. To overcome the serious drawbacks of the previous frameworks, this paper provides a robust SLAM algorithm based on the Sterling polynomial interpolation. It uses the central difference filter (CDF) to compute the proposal distribution in Rao-Blackwellized particle filter, then to initialize and update each feature state. For practical application, an effective mechanism for feature management is proposed. This approach improves the state estimation accuracy, and requires a smaller number of particles than previous approaches. Both simulation and experimental results are used to validate the effectiveness of the proposed algorithm.