重庆理工大学学报(自然科学) ›› 2023, Vol. 37 ›› Issue (6): 119-128.

• “智能机器人感知、规划及应用技术”专栏 • 上一篇    下一篇

椭球语义对象辅助的 RGBD相机重定位方法

林中文,曾 碧,刘建圻   

  1. (广东工业大学 计算机学院,广州 510006)
  • 出版日期:2023-07-12 发布日期:2023-07-12
  • 作者简介:林中文,男,硕士研究生,主要从事 SLAM研究,Email:1624232490@qq.com;通信作者 曾碧,女,博士,教授,主 要从事智能机器人,智能信息处理等方面的研究,Email:zb9215@gdut.edu.cn。

An RGB-D camera relocalization method aided by ellipsoidal semantic objects

  • Online:2023-07-12 Published:2023-07-12

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(iterativeclosestpoint)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(randomferns,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。

关键词: 视觉重定位, RGBD, AR, 语义地图

Abstract: 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.

中图分类号: 

  • TP242