基于RGB-D图像的室内机器人同时定位与地图构建

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

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Pn P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。

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 RGBDepth(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(Pn P)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 3 D map for robot navigation.On the TUM RGB-D dataset,the proposed RGBD 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.