一种基于三维视觉的移动机器人定位与建图方法  

A method of mobile robot localization and mapping based on 3D vision

作　　者：陈超[1] 张伟伟 徐军 CHEN Chao;ZHANG Weiwei;XU Jun(School of Mechanical Engineering,Jiangsu University of Science and Technology,Zhenjiang 212000,China)

机构地区：[1]江苏科技大学机械工程学院,江苏镇江212000

出　　处：《现代电子技术》2020年第6期34-38,42,共6页Modern Electronics Technique

基　　金：国家自然科学基金资助项目(51705217)。

摘　　要：针对移动机器人三维视觉SLAM(同步定位与建图)中定位精度低、实时性差等问题,提出一种基于由初到精的位姿估计和双重闭环策略的SLAM方法。首先对MSER(最大稳定极值区)检测算法进行椭圆拟合化处理并提取出图像中的ROI(感兴趣区);然后从ROI中提取出稀疏像素点并使用直接法得到初始位姿变换参数;接着结合改进的基于八叉树结构的ICP(迭代最近点)对相机位姿进行精估计;再结合关键帧选择机制提出一种双重闭环检测方法为构建的位姿图添加约束;最后通过g2o图优化框架对位姿图进行优化并完成点云的拼接。通过NYU和TUM标准数据集验证了算法的实时性与有效性,室内实验结果表明,在复杂环境下也能利用该方法进行准确的位姿估计,并构建出环境的三维点云地图。A SLAM method based on the initial to precise pose estimation and double closed-loop strategy is proposed to improve the positioning accuracy and real-time performance in the 3D vision SLAM(synchronous localization and mapping)of mobile robot.The ellipse fitting processing is performed for the MSER(maximally stable extremal region)detection algorithm and the ROI(region of interest)in the image is extracted,and then the sparse pixels are extracted from ROI and the initial pose transformation parameters are obtained by means of the direct method.The camera pose is accurately estimated in combination with the improved ICP(iterative nearest point)based on octree structure.A double closed-loop detection method is proposed in combination with the key frame selection mechanism to add constraints to the constructed pose map.By means of the g2o map optimization framework,the pose map is optimized and the splicing of point cloud is completed.The real-time performance and effectiveness of the algorithm is verified with the NYU and TUM standard data sets.The results of indoor experiments show that in a complex environment,this method can also be used to accurately estimate the pose and construct a 3D point cloud map of the environment.

关 键 词：SLAM 移动机器人 三维视觉 位姿估计 闭环检测 点云拼接 

分 类 号：TN95-34[电子电信—信号与信息处理] TP242[电子电信—信息与通信工程]