Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (12): 3637-3643.DOI: 10.11772/j.issn.1001-9081.2020040518

• Virtual reality and multimedia computing • Previous Articles     Next Articles

Indoor robot simultaneous localization and mapping based on RGB-D image

ZHAO Hong, LIU Xiangdong, YANG Yongjuan   

  1. School of Computer and Communications, Lanzhou University of Technology, Lanzhou Gansu 730050, China
  • Received:2020-04-23 Revised:2020-08-10 Online:2020-12-10 Published:2020-08-21
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (51668043, 61262016), the Next Generation Internet Technology Innovation Project of CERNET (NG1120160311, NG1120160112).


赵宏, 刘向东, 杨永娟   

  1. 兰州理工大学 计算机与通信学院, 兰州 730050
  • 通讯作者: 刘向东(1994-),男,甘肃陇西人,硕士研究生,CCF会员,主要研究方向:三维重建、视觉SLAM。
  • 作者简介:赵宏(1971-),男,甘肃西和人,教授,博士生导师,博士,CCF会员,主要研究方向:并行与分布式处理、三维重建、深度学习;杨永娟(1996-),女,甘肃靖远人,硕士研究生,CCF会员,主要研究方向:三维重建、视觉惯性SLAM
  • 基金资助:

Abstract: Simultaneous Localization and Mapping (SLAM) is a key technology for robots to realize autonomous navigation in unknown environments. Aiming at the poor real-time performance and low accuracy of the commonly used RGB-Depth (RGB-D) SLAM system, a new RGB-D SLAM system was proposed to further improve the real-time performance and accuracy. Firstly, the Oriented FAST and Rotated BRIEF (ORB) algorithm was used to detect the image feature points, and the extracted feature points were processed by using the quadtree-based homogenization strategy, and the Bag of Words (BoW) was used to perform feature matching. Then, in the stage of system camera pose initial value estimation, an initial value which was closer to the optimal value was provided for back-end optimization by combining the Perspective n Point (PnP) and nonlinear optimization methods. In the back-end optimization, the Bundle Adjustment (BA) was used to optimize the initial value of the camera pose iteratively for obtaining the optimal value of the camera pose. Finally, according to the correspondence between the camera pose and the point cloud map of each frame, all the point cloud data were registered in a coordinate system to obtain the dense point cloud map of the scene, and the octree was used to compress the point cloud map recursively, so as to obtain a 3D map for robot navigation. On the TUM RGB-D dataset, the proposed RGB-D SLAM system, RGB-D SLAMv2 system and ORB-SLAM2 system were compared. Experimental results show that the proposed RGB-D SLAM system has better comprehensive performance on real-time and accuracy.

Key words: RGB-Depth (RGB-D) senor, Simultaneous Localization and Mapping (SLAM), dense point cloud map, octo-map

摘要: 同时定位与地图构建(SLAM)是机器人在未知环境实现自主导航的关键技术,针对目前常用的RGB-D SLAM系统实时性差和精确度低的问题,提出一种新的RGB-D SLAM系统,以进一步提升实时性和精确度。首先,采用ORB算法检测图像特征点,并对提取的特征点采用基于四叉树的均匀化策略进行处理,并结合词袋模型(BoW)进行特征匹配。然后,在系统相机姿态初始值估计阶段,结合PnP和非线性优化方法为后端优化提供一个更接近最优值的初始值;在后端优化中,使用光束法平差(BA)对相机姿态初始值进行迭代优化,从而得到相机姿态的最优值。最后,根据相机姿态和每帧点云地图的对应关系,将所有的点云数据注册到同一个坐标系中,得到场景的稠密点云地图,并对点云地图利用八叉树进行递归式的压缩以得到一种用于机器人导航的三维地图。在TUM RGB-D数据集上,将构建的RGB-D SLAM同RGB-D SLAMv2、ORB-SLAM2系统进行了对比,实验结果表明所构建的RGB-D SLAM系统在实时性和精确度上的综合表现更优。

关键词: RGB-D传感器, 同时定位与地图构建, 稠密点云地图, 八叉树地图

CLC Number: