A Laser SLAM Algorithm for Indoor Dynamic Pedestrian Scenes
Eliminating the interference of dynamic pedestrians in real-time mapping is a core challenge in laser Simultaneous Localization And Mapping(SLAM)algorithms,particularly in complex indoor environments.Most existing SLAM algorithms focus primarily on static scenes and overlook the presence of moving objects.However,in indoor environments,the frequent appearance of moving pedestrians significantly degrades the quality of the global point-cloud map and increases uncertainty in subsequent localization and navigation tasks.To address this issue,this study proposes a tightly coupled laser SLAM algorithm specifically designed for dynamic pedestrian scenarios in indoor environments,with the aim of better adapting to such complex scenarios.In addition to the traditional SLAM framework,this study introduces a pre-processing module based on point-cloud clustering and segmentation to accurately eliminate dynamic pedestrian point clouds.Our algorithm first applies an enhanced two-stage clustering algorithm based on the Euclidean distance to cluster and segment point clouds.Subsequently,multidimensional slice and intensity features are extracted from the clustering results and combined with the classification results of a Support Vector Machine(SVM)to identify pedestrian instances at the scene.Meanwhile,the algorithm utilizes a static point cloud to estimate ego motion in real time and constructs a high-resolution point cloud map.To evaluate the performance of the algorithm,assessments are performed on both the Hilti public dataset and real-world scenario data,specifically focusing on the effectiveness of dynamic point-cloud removal and real-time capability.Experimental results demonstrate that the algorithm significantly improves the point cloud map construction quality and remarkably reduces the proportion of dynamic pedestrian points compared to state-of-the-art laser SLAM algorithms such as Removert and Dynablox.The processing time of the system for a single frame image does not exceed 100 ms,meeting real-time requirements.
Simultaneous Localization And Mapping(SLAM)multi-sensor fusiondynamic pedestriantightly couplingpoint cloud processing
万方数据
维普
