Improved Monte Carlo Localization Based on Probability Density Maps for Autonomous Mobile Robots

Among the widely applied localization algorithms, Monte Carlo localization (MCL) stands out due to its nonlinear nature. MCL utilizes occupancy grid maps for particle filtering-based localization. However, the current methods used to construct occupancy grid maps often discretize the environment into grid cells, limiting their ability to represent the environment accurately. The optimization trade-off challenges the performance of MCL. In this paper, an improved MCL method (MCL-PD) is proposed based on probability density maps to address this issue. The continuous probability density maps are constructed using lidar data, allowing the map to dynamically change during the localization process. Additionally, the map's scoring range and morphology are adjusted based on particle weights to amplify the gradient of the particle weight, ensuring the selection of the optimal particle. Experimental results demonstrate that our proposed MCL-PD method outperforms classical algorithms such as MCL, Normal Distribution Transform (NDT), and Iterative Closest Point (ICP) in terms of localization accuracy.