[1]张毅,沙建松.基于图优化的移动机器人视觉SLAM[J].智能系统学报,2018,13(02):290-295.[doi:10.11992/tis.201612004]
 ZHANG Yi,SHA Jiansong.Visual-SLAM for mobile robot based on graph optimization[J].CAAI Transactions on Intelligent Systems,2018,13(02):290-295.[doi:10.11992/tis.201612004]
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基于图优化的移动机器人视觉SLAM(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第13卷
期数:
2018年02期
页码:
290-295
栏目:
出版日期:
2018-04-15

文章信息/Info

Title:
Visual-SLAM for mobile robot based on graph optimization
作者:
张毅 沙建松
重庆邮电大学 智能系统及机器人实验室, 重庆 400065
Author(s):
ZHANG Yi SHA Jiansong
Laboratory of Intelligent System and Robotics, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
关键词:
RGB-D移动机器人图优化同时定位与地图构建位姿与点云优化
Keywords:
RGB-Dmobile robotgraph optimizationSLAMpose and point cloud optimization
分类号:
TP24
DOI:
10.11992/tis.201612004
摘要:
针对目前移动机器人视觉SLAM(simultaneous localization and mapping)研究中存在的实时性差、精确度不高、无法稠密化建图等问题,提出了一种基于RGB-D数据的实时 SLAM算法。在本算法前端处理中,采用了鲁棒性与实时性更好的ORB特征检测。利用 RANSAC 算法对可能存在的误匹配点进行剔除完成初始匹配,对所得内点进行PNP求解,用于机器人相邻位姿的增量估计。在后端优化中,设计了一种遵循图优化思想的非线性优化方法对移动机器人位姿进行优化。同时结合闭环检测机制,提出了一种点云优化算法,用于抑制系统的累积误差,进一步提升位姿与点云的精确性。实验验证了本文所提方法能够迅速、准确地重构出稠密化的三维环境模型。
Abstract:
In the present research on the visual SLAM (Simultaneous Localization and Mapping)of mobile robot, some defects as inferior real-timeness, low accuracy and hard to densified mapping exist, therefore, the paper proposed a real-time SLAM algorithm based on RGB-D data. In the front-end processing of the algorithm, the ORB feature detection with better robustness and real-timeness was adopted. RANSAC algorithm was utilized to get rid of the possible mismatch points and complete the initial match. For the obtained inner point, PNP solution was carried out for using as the increment estimate of the adjacent pose of robot. In the rear-end optimization, a nonlinear optimization method obeying image optimization thought was used for optimizing the pose of a moving robot. In addition, in combination with the closed-loop detection mechanism, a point cloud optimization algorithm was proposed to suppress the cumulative error of the system and further improve the accuracy of pose and point cloud. The experimental results show that the proposed method can reconstruct the dense 3D environment model quickly and accurately.

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备注/Memo

备注/Memo:
收稿日期:2016-12-05。
基金项目:重庆市科学技术委员会项目(CSTC2015jcyjBX0066).
作者简介:张毅,男,1966年生,教授,博士生导师,中国人工智能学会理事,国家信息无障碍研发中心主任,主要研究方向为智能系统与移动机器人、机器视觉与模式识别、多传感器信息融合。主持完成国家级和省部基金项目10余项,发表学术论文100余篇,被SCI、EI和ISTP收录30余篇次,出版著作5部,获国家发明专利30余项;沙建松,男,1991年生,硕士研究生,主要研究方向为机器人同时定位与地图创建 (SLAM)、基于语义信息的机器人三维视觉导航。
通讯作者:沙建松.E-mail:277829172@qq.com.
更新日期/Last Update: 1900-01-01