基于RGB-D的云机器人3D SLAM实验系统

3D SLAM experimental system of cloud robot based on RGB-D

针对室内环境的三维视觉同步定位与地图构建(3D VSLAM)计算量大、耗时长、硬件要求高的问题,提出了一种基于RGB-D的云机器人VSLAM实验平台。采用Kinect传感器,获取环境的RGB图像和深度信息,采用金字塔Lucas-Kanade算法实现帧间FAST特征点的快速追踪与匹配,运用RANSAC算法进行初始配准,提取关键帧。借助于云计算动态供给、弹性计算的优势,将VSLAM中计算消耗大的精确配准、闭环检测和全局优化处理过程卸载至云端进行,以减轻本地处理器的运算负担。实验结果表明,该方法能够有效地减轻VSLAM对硬件的依赖度,缩短SLAM的执行时间并提高构图精度,为云机器人以较低的成本实现先进SLAM算法提供了有效的解决途径。

In view of the problems of large computation,time-consuming and high hardware requirements of 3D-VSLAM in indoor environment,a cloud robot VSLAM experimental platform based on RGB-D is proposed. The Kinect sensor is used to acquire RGB image and depth information of the environment,the pyramid Lucas-Kanade algorithm is adopted to realize the fast tracking and matching of FAST feature points between frames,and the RANSAC algorithm is used for initial registration and key frame extraction. By virtue of the advantages of dynamic supply and flexible computation in cloud computing,the process of the accurate registration,closed-loop detection and global optimization in VSLAM is unloaded to the cloud to reduce the computing burden of local processors. The experimental results show that this method can effectively reduce the hardware dependence of VSLAM,shorten the execution time of SLAM and improve the composition accuracy. It provides an effective solution for cloud robots to implement the advanced SLAM algorithm at a lower cost.