The computational complexity of the most popular particle filtering SLAM algorithms are linear proportional to the number of landmarks, which have obvious computational superiority for dense map or largescale SLAM . However, there is no guarantee that the computed covariance will match the actual estimation errors, which is the true SLAM consistency problem. The lack of consistency of these algorithms will lead to filter divergence. In order to ensure consistency, a new particle filtering SLAM algorithm is proposed, which is based on the marginal particle filtering and using unscented Kalman filtering (UKF) to generate proposal distributions. The underlying algorithm operates directly on the marginal distribution, hence avoiding having to perform importance sampling on a space of growing dimension. Additionally, UKF can reduce linearization error and gain accurate proposal distributions. Compared with the common particle filtering SLAM methods, the new algorithm increases the number of effective particles and reduces variance of particles weight effectively. Also, it is consistent owing to the better particle diversity. As a result, it does not suffer from some shortcomings of existing particle methods for SLAM and has distinct superiority. Finally, plentiful simulations are carried out to evaluate the algorithm’s performance and the results indicate that the algorithm is valid.