摘要
针对室内场景的复杂性和封闭性导致重建出的室内三维模型耗时较长、覆盖度不佳的问题,提出一种利用地磁特征辅助的室内运动恢复结构(SFM)方法。首先,利用普通智能手机传感器采集室内的影像和地磁数据;其次,为实现整体影像集划分为局部影像集,通过聚类算法将地磁数据进行聚类,并将地磁数据聚类结果作为对应影像的属性得到局部影像集;然后,运用分层式SFM对各局部影像集进行稀疏子模型构建,并对各个稀疏子模型间匹配点进行确定;最后,利用RANSAC generalized Procrustes analysis(RGPA)算法实现各局部重建的配准,得到完整模型。室内同楼层和不同楼层的重建实验结果表明,所提方法在重建效率、重建覆盖度和点云生成速率方面表现较好,相比分层式SFM方法,其重建效率在两个数据集上平均提升了37%,重建覆盖度更接近重建目标,为同种类型室内环境重建提供了一种补充方案。
To solve the problems that the complexity and closure of indoor scenes lead to the time-consuming and poor coverage of the reconstructed indoor 3D model,a method for indoor structure from motion(SFM)assisted by geomagnetic features is proposed.First,ordinary smartphone sensors were used to obtain indoor images and geomagnetic data.Second,to divide the overall image set into local image sets,a clustering algorithm was used to cluster geomagnetic data,and the clustering results of the geomagnetic data were used as attributes of the corresponding images to obtain the local image sets.Subsequently,the hierarchical SFM was used to construct sparse sub models for each local image set,and the matching points between each sparse sub model were determined.Finally,the RANSAC generalized Procrustes analysis(RGPA)algorithm was used to register local reconstructions and obtain a complete model.Experimental results of indoor reconstruction on the same and different floors show that the proposed method performs well in terms of reconstruction efficiency,reconstruction coverage,and point-cloud generation rate.Compared with the hierarchical SFM method,the proposed method offers a higher reconstruction efficiency by 37%on both datasets,and its reconstruction coverage is closer to the reconstruction target,thus providing a supplementary solution for constructing the same type of indoor environment.
作者
何周猛
陈国良
束明聪
狄开宇
刘虎
He Zhoumeng;Chen Guoliang;Shu Mingcong;Di Kaiyu;Liu Hu(School of Environment and Spatial Informatics,China University of Mining and Technology,Xuzhou 221116,Jiangsu,China;Key Laboratory of Land Environment and Disaster Monitoring of Ministry of Natural Resources,China University of Mining and Technology,Xuzhou 221116,Jiangsu,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2024年第18期421-428,共8页
Laser & Optoelectronics Progress
基金
国家自然科学基金(42274048)
江苏省重点研发计划(BE2022716)。
关键词
地磁特征
聚类算法
室内环境
分层式运动恢复结构
点云配准
geomagnetic features
clustering algorithm
indoor environment
hierarchical structure from motion
point cloud registration
