Abstract: The traditional FastSLAM algorithm requires a large number of particles to build maps, and the particle degeneracy will lead to high computational complexity and difficulties in improving the estimation accuracy. For those problems, a fast SLAM (simultaneous localization and mapping) algorithm based on non-parametric Bayesian learning of Dirichlet process for Gauss box particle filter is proposed. Firstly, the uniform probability density function with box particles as the support set in box particle filter is improved, and the Gauss probability density function is used to carry out Bayesian filtering in order to improve the estimation accuracy. On this basis, non-parametric Bayesian learning of Dirichlet process is applied to resampling Gauss box particles, which not only ensures an effective number of box particles, but also enables the box particles to concentrate in the high likelihood region, and thus weakening the effect of sample exhaustion. Then, the non-parametric Bayesian learning of Dirichlet process for Gauss box particle filter is used to estimate the robot pose, which reduces the number of particles needed to build maps and improves the location accuracy and real-time performance. Further, the unscented Kalman filter is used to update the features in the map, which improves the consistency of map building. Simulation and experiment results on the wheel-legged robot verify the feasibility and effectiveness of the proposed method.