Traditional data association-based simultaneous localization and mapping (SLAM) methods are prone to causing mismatches between observations and targets, leading to a decrease in pose estimation accuracy. This paper combines a column feature extraction method and random finite set theory to propose a 3D LiDAR SLAM method for cluttered environments based on sequential Monte Carlo implementation. Firstly, stable column features are extracted from segmented point clouds using the M-estimator sample consensus algorithm to obtain static surviving features and new features within a single frame of point cloud data. Subsequently, two types of features are introduced into the RB-PHD-SLAM framework, and the sequential Monte Carlo method is employed to achieve inter-frame propagation of the vehicle’s trajectory probability density and the map posterior. This enables simultaneous estimation of environmental features and vehicle poses. Evaluation results based on both simulation dataset and KITTI dataset show that, compared to the classical FastSLAM algorithm, the proposed method improves the vehicle positioning accuracy by more than 40%, and reduces the average estimation error of feature location and feature number by more than 40% and 50%, respectively. This indicates that the proposed method significantly improves the accuracy and robustness of SLAM, and helps to ensure safe operation of intelligent vehicles.