计算机工程与应用2024，Vol.60Issue(20)：312-319,8.DOI:10.3778/j.issn.1002-8331.2306-0225
用于SLAM的点云动态物体识别
Point Cloud Dynamic Object Recognition for SLAM
摘要
Abstract
Detecting and segmenting dynamic objects in a scene is crucial for building a consistent map.A dynamic object detection algorithm based on continuous point clouds is proposed to address the problems that current point cloud dynamic object detection algorithms rely on a large amount of data containing dynamic attribute annotations and limit the scanning method of LiDAR.The input for this algorithm consists of the point cloud to be predicted,adjacent frame point clouds,and pose information obtained through simultaneous localization and mapping(SLAM).A point cloud scene flow estimation algorithm is employed by this algorithm to estimate motion at a per-point level,and scene flow results are inte-grated using techniques such as point cloud clustering and principal component analysis(PCA)to infer instance-level motion information for the determination of dynamic object attributes.Furthermore,point cloud semantic segmentation is utilized as a plug-in to boost dynamic object recognition accuracy by categorizing points into movable categories.The need for annotated data with dynamic attributes for training is eliminated by the proposed algorithm,and it does not impose any restrictions on sensor scanning methods or the number of generated point clouds.Comparative analysis with state-of-the-art methods demonstrates the algorithm's ease of training,high accuracy in object classification,and robustness.关键词
即时定位与地图构建(SLAM)/深度学习/点云Key words
simultaneous localization and mapping(SLAM)/deep learning/point clouds分类
信息技术与安全科学引用本文复制引用
代领,宋振波,陆建峰..用于SLAM的点云动态物体识别[J].计算机工程与应用,2024,60(20):312-319,8.
