Optimization and Implementation of RBPF-SLAM Algorithms for Autonomous Mobile Robots

A simultaneous localization and map construction (RBPF-SLAM) technique with improved resampling RaoBlackwellized particle filtering is suggested to accomplish accurate and efficient simultaneous localization and map building (SLAM) for mobile robots in complicated situations. The classic RBPF algorithm implementation’s high error proposal distribution samples a large number of particles to meet the target distribution, and the frequent resampling steps cause particles to dissipate over time. The adaptive partitioning resampling approach replaces the significance resampling method in the original algorithm in this study, and the experimental findings demonstrate that the new algorithm can greatly enhance computational efficiency and create more accurate maps.