陈劭,郭宇翔,高天啸,宫清源,张军国.移动机器人RGB-D视觉SLAM算法[J].农业机械学报,2018,49(10):38-45.
CHEN Shao,GUO Yuxiang,GAO Tianxiao,GONG Qingyuan,ZHANG Junguo.RGB-D Visual SLAM Algorithm for Mobile Robots[J].Transactions of the Chinese Society for Agricultural Machinery,2018,49(10):38-45.
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移动机器人RGB-D视觉SLAM算法   [下载全文]
RGB-D Visual SLAM Algorithm for Mobile Robots   [Download Pdf][in English]
投稿时间:2018-05-05  
DOI:10.6041/j.issn.1000-1298.2018.10.005
中文关键词:  移动机器人  RGB-D  视觉同步定位与地图构建  广义迭代最近点  图优化
基金项目:中央高校基本科研业务费专项资金项目(2016ZCQ08)和国家级大学生创新创业训练项目(20170022057)
作者单位
陈劭 北京林业大学 
郭宇翔 北京林业大学 
高天啸 北京林业大学 
宫清源 北京林业大学 
张军国 北京林业大学 
中文摘要:针对移动机器人视觉同步定位以及地图构建(Simultaneous localization and mapping, SLAM)研究中存在精确度较低、实时性较差等问题,提出了一种用于移动机器人的RGB-D视觉SLAM算法。首先利用定向二进制简单描述符(Oriented fast and rotated brief, ORB)算法提取RGB图像的特征点,通过基于快速近似最邻近(Fast library for approximate nearest neighbors, FLANN)的双向邻近(K-nearest neighbor, KNN)特征匹配方法得到匹配点对集合,利用改进后的随机抽样一致性(Re-estimate random sample consensus, RE-RANSAC) 算法剔除误匹配点,估计得到相邻图像间的6D运动变换模型,然后利用广义迭代最近点(Generalized iterative closest point, GICP)算法得到优化后的运动变换模型,进而求解得到相机位姿。为提高定位精度,引入随机闭环检测环节,减少了机器人定位过程中的累积误差,并采用全局图优化(General graph optimization, G2O)方法对相机位姿图进行优化,得到全局最优相机位姿和相机运动轨迹;最终通过点云拼接生成全局彩色稠密点云地图。针对所测试的FR1数据集,本文算法的最小定位误差为0.011m,平均定位误差为0.0245m,每帧数据平均处理时间为0.032s,满足移动机器人快速定位建图的需求。
CHEN Shao  GUO Yuxiang  GAO Tianxiao  GONG Qingyuan  ZHANG Junguo
Beijing Forestry University,Beijing Forestry University,Beijing Forestry University,Beijing Forestry University and Beijing Forestry University
Key Words:mobile robot  RGB-D  visual simultaneous localization and mapping  generalized iterative closest point  figure optimization
Abstract:In view of the problems of low accuracy and poor real-time in the research of visual simultaneous localization and mapping, a RGB-D vision SLAM algorithm for indoor mobile robots was proposed. Firstly, feature points of RGB image were extracted by using oriented fast and rotated brief (ORB) algorithm, and matching point pair set was obtained by the bidirectional K-nearest neighbor (KNN) feature matching method based on fast library for approximate nearest neighbors (FLANN). The improved random sampling consistency algorithm (RE-RANSAC) was used to eliminate false matching points and estimate the 6D motion transformation model between two adjacent images, as the initial transformation model of GICP algorithm. The generalized iterative closest point algorithm (GICP) was used to obtain the optimized motion transformation model, and then the pose diagram was obtained. In order to improve the positioning accuracy, a random closed-loop detection link was introduced to reduce the cumulative error in the robot positioning process, and the pose diagram was optimized by using the general graph optimization (G2O) method to obtain the global optimal pose diagram and camera motion trajectory, and the global color dense point cloud map was finally generated. For the tested FR1 data sets, the minimum positioning error of the algorithm was 0.011m, the average positioning error was 0.0245m, and the average processing time of each frame was 0.032s, which can meet the requirement of rapid positioning and mapping of mobile robots.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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