To achieve real-time positioning and map construction in an unknown harsh environment with low precision and poor reliability, and realize accurate, efficient simultaneous localization and mapping (SLAM) of overloaded robots in an unknown environment, this paper proposes a SLAM algorithm to improve resampling by Rao-Blackwellized particle filtering. In the Rao-Blackwellized particle filters SLAM (RBPF-SLAM) algorithm, by using high-precision radar data, the odometer reading-based motion model is employed as the proposed distribution function, and the proposed distribution function based on the odometer reading is performed, thereby greatly reducing the number of particles. A particle weight balancing strategy was introduced during resampling to solves the problem of particle weight degradation and particle starvation caused by inaccurate grid map construction so that the map is perfectly matched with the actual environment, which greatly improves the construction efficiency of the crawler-type heavy-duty robot in an unknown environment to save computing resources. The improved algorithm and the basic RBPF-SLAM algorithm are compared in simulated environment and physical environment separately. The improved RBPF-SLAM algorithm flow is distinguished from the basic RBPF-SLAM algorithm in the resampling process. In the improved resampling process, particles are sampled according to the particle weight value, so the weight value is preprocessed and optimized.Experimental results show that the proposed RBPF-SLAM algorithm is better than the basic RBPF-SLAM algorithm in both the virtual environment and the real environment. The map is more accurate and faster, and the effectiveness of the improved algorithm is verified. The follow-up work will focus on the comparison of the improved algorithm with the latest algorithm and the verification in an unstructured experimental environment.