摘要
在复杂室内环境中,消除动态行人对实时建图的干扰一直是激光同步定位与建图(SLAM)算法需要解决的核心问题之一。当前的SLAM算法主要关注静态场景,忽略了场景中存在的运动物体。然而,在室内场景中,频繁出现的移动行人降低了全局点云地图的质量,增加了后续定位与导航的不确定性。提出一种针对室内动态行人场景的紧耦合激光SLAM算法,以更好地适应此类复杂场景。在传统SLAM框架的基础上引入基于点云聚类与分割的预处理模块,用于准确消除动态行人点云。该算法首先采用基于欧氏距离的增强两步式聚类算法对点云进行聚类和分割,随后提取聚类结果的多维切片特征和强度特征,并结合支持向量机(SVM)的分类结果来识别场景中的行人实例,同时利用静态点云实时估计自身位姿并构建高分辨率点云地图。分别使用Hilti公开数据集以及真实场景数据对所提算法的动态点云去除效果和实时性进行测试,结果表明,相较于Removert、Dynablox等当前先进的激光SLAM算法,该算法能够显著改善点云地图的构建效果,降低其中动态行人点云的比例,且系统对单帧图片的处理时长不超过100 ms,满足实时性要求。
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 twostage 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-ofthe-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.
作者
叶智奇
章国宝
朱宏伟
YE Zhiqi;ZHANG Guobao;ZHU Hongwei(School of Automation,Southeast University,Nanjing 210000,Jiangsu,China)
出处
《计算机工程》
CAS
CSCD
北大核心
2024年第6期208-217,共10页
Computer Engineering
基金
江苏省重点研发项目(BE2021750)。
关键词
同步定位与建图
多传感器融合
动态行人
紧耦合
点云处理
Simultaneous Localization And Mapping(SLAM)
multi-sensor fusion
dynamic pedestrian
tightly coupling
point cloud processing
