椭球语义对象辅助的RGB-D相机重定位方法  

An RGB-D camera relocalization method aidedby ellipsoidal semantic objects

作　　者：林中文 曾碧[1] 刘建圻[1] 温俊斌 江明 LIN Zhongwen;ZENG Bi;LIU Jianqi;WEN Junbin;JIANG Ming(School of Computer Science and Technology,Guangdong University of Technology,Guangzhou 510006,China)

机构地区：[1]广东工业大学计算机学院,广州510006

出　　处：《重庆理工大学学报（自然科学）》2023年第6期119-128,共10页Journal of Chongqing University of Technology：Natural Science

基　　金：国家自然科学基金项目(62172111,U21A20478);广东省自然科学基金项目(2019A1515011056)。

摘　　要：提出了一种基于椭球语义对象的RGB-D重定位方法。首先,在相机跟踪过程中,利用深度点云对观测对象进行单帧初始化,采用椭球体描述被观测的地图对象,通过对象共视图关联地图对象和相应的目标检测结果,以构建椭球对象级语义地图;然后,基于地图对象的位置进行相机重定位;最后,利用ICP(iterative closest point)点云配准算法优化相机位姿。OR10数据集测试表明,在词袋方法(bag-of-words, BoW)和随机蕨方法(random ferns, FERNS)表现较差的大视差环境下,该重定位方法仍能有较高的成功率,且算法运行时间与这2种方法相近。This paper proposes an RGB-D relocalization method based on ellipsoidal semantic objects.Firstly,in the process of camera tracking,this paper initializes the observation object in a single frame through combining the depth point cloud,and describes the observed map object by an ellipsoid.An object-level semantic map represented by an ellipsoid is constructed by associating map objects and the corresponding target detection results by using an object common view.Then,the camera is relocated based on the location of the map object.Finally,Iterative Closest Point(ICP)cloud registration algorithm is used to further optimize the camera pose.The experimental results on the OR10 data set show that the relocation method achieves a high relocation success rate in a large parallax environment where the bag-of-words(BoW)and random ferns(FERNS)methods perform poorly.In addition,the proposed algorithm has a similar running time to the above two methods.

关 键 词：视觉重定位 RGB-D AR 语义地图 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]