This paper proposes a reduced sigma point transformation for a FastSLAM framework. The sigma point transformation is used to estimate robot poses in conjunction with generic particle filter used in standard FastSLAM framework. This method can estimate robot poses more consistently and accurately than the current standard particle filters, especially when involving highly nonlinear models or non-Gaussian noises. In addition, this algorithm avoids the calculation of the Jacobian for motion model which could be extremely difficult for high order systems. We proposed a sampling strategy known as a spherical simplex for sigma point transformation to estimate robot poses in FastSLAM framework. Simulation results are shown to validate the performance goals
