基于Kinect传感器和ORB特征的视觉SLAM算法设计与实现

摘要

介绍了一个基于嵌入式平台和Kinect传感器的同时定位与地图创建算法的设计与实现.Kinect传感器包括一个可见光彩色摄像头和一个利用结构光测量深度的红外CMOS摄像头.算法利用ORB算子作为环境特征点的描述信息,并利用基于边沿的最近邻修复方法对深度图像进行修正以获得完整的深度信息.在此基础上,利用LSH方法进行特征点的匹配.实验结果表明,基于ORB特征的视觉SLAM算法具有较好的实用性和良好的定位精度,可以广泛应用于室内机器人的自主导航任务.%This paper designs and implements a visual Simultaneous Localization And Mapping (SLAM) algorithm based on embedded platform and Kinect sensors.Kinect sensors consist of a RGB camera and an infrared CMOS camera that uses structured light to measure depth.The algorithm uses the Oriented FAST and Rotated BRIEF (ORB) operator as the description information of the environmental feature points and uses the nearest neighbor repairing method based on edge detection to correct the depth image so as to obtain a complete depth map.Based on this,Locality-Sensitive Hashing (LSH)algorithm is used to match feature points.Experimental results show that the visual SLAM algorithm based on ORB features is feasible,has good positioning accuracy,and can be widely used in autonomous navigation tasks of indoor robots.