Improved PSO-Gmapping Algorithm for Localization and Mapping Applied in Unmanned Ground Vehicles

Although the traditional Gmapping algorithm incorporates optimized proposal distribution and resampling strategies within the RBPF-SLAM framework, it remains susceptible to particle degradation during intensive particle iterations. This degradation compromises map integrity and localization accuracy. To address this limitation, this study proposes an enhanced Gmapping system integrated with an improved particle swarm optimization (PSO) algorithm. The proposed PSO incorporates an adaptive inertia weight and a Gaussian distribution model to guide swarm dynamics, thereby effectively accelerating convergence. Furthermore, during the resampling phase, the system adopts an SDPR strategy to reduce computational complexity, shorten runtime, and alleviate particle degradation. The improved PSO algorithm was first validated through MATLAB R2022b simulations, and the integrated system was subsequently implemented and tested on a ROS-based Unmanned ground vehicle (UGV) platform within the Gazebo simulation environment (Gazebo Garden). From the results, compared with classical Gmapping using 50 particles, the proposed method using 50 particles reduces the ATE RMSE from 0.154 m to 0.104 m, corresponding to a 32.5% reduction. The RPE translation RMSE decreases by 31.0%, and the map-scale MAE decreases by 44.6%. The average time per frame is also slightly lower than Gmapping-50 because SDPR reduces the frequency and cost of full resampling. Experimental results demonstrate that the proposed system yields significant improvements in both accuracy and robustness for localization and environmental mapping.