基于改进闭环检测算法的视觉同时定位与地图构建

doi:10.11772/j.issn.1001-9081.2017082004

计算机应用 ›› 2018, Vol. 38 ›› Issue (3): 873-878.DOI: 10.11772/j.issn.1001-9081.2017082004

• 虚拟现实与多媒体计算 • 上一篇下一篇

基于改进闭环检测算法的视觉同时定位与地图构建

胡章芳¹, 鲍合章¹, 陈旭¹, 范霆铠¹, 赵立明²

1. 重庆邮电大学光电工程学院, 重庆 400065;
2. 重庆邮电大学先进制造工程学院, 重庆 400065

收稿日期:2017-08-17 修回日期:2017-10-15 出版日期:2018-03-10 发布日期:2018-03-07
通讯作者: 鲍合章
作者简介:胡章芳(1969-),女,重庆渝北人,副教授,硕士,主要研究方向:光电信息处理;鲍合章(1993-),男,安徽六安人,硕士研究生,主要研究方向:视觉导航;陈旭(1995-),男,吉林长春人,主要研究方向:图像处理;范霆铠(1997-),男,重庆北碚人,主要研究方向:图像处理;赵立明(1981-),男,河北秦皇岛人,讲师,博士,主要研究方向:机器视觉。
基金资助:
国家自然科学基金资助项目（51604056）；重庆科委自然科学基金资助项目（cstc2016jcyjA0537）。

Visual simultaneous location and mapping based on improved closed-loop detection algorithm

HU Zhangfang¹, BAO Hezhang¹, CHEN Xu¹, FAN Tingkai¹, ZHAO Liming²

1. School of Optoelectronic Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
2. School of Advanced Manufacturing Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Received:2017-08-17 Revised:2017-10-15 Online:2018-03-10 Published:2018-03-07
Supported by:
This work is partially supported by the National Natural Science Foundation of China (51604056), the Natural Science Foundation of Chongqing Science and Technology Commission (cstc2016jcyjA0537).

摘要/Abstract

摘要： 针对视觉同时定位与地图构建（SLAM）中容易由误差累积导致构建地图不一致的问题，提出了一种基于改进闭环检测算法的视觉SLAM（V-SLAM）系统。为了减小移动机器人长时间运行带来的累计误差，引入一种改进的闭环检测算法，改进相似性得分函数，减小感知歧义，提高闭环的识别率；同时为了减小计算量，通过Kinect直接获取环境图像以及深度信息，并采用计算量小、鲁棒性好的ORB特征进行特征提取和匹配；采用随机抽样一致性（RANSAC）算法进行误匹配删除，从而获得更准确的匹配点对，然后用PnP计算出相机位姿；更稳定、准确的初始估计位姿对后端处理至关重要，利用g2o对位姿进行无结构的迭代优化；最后在后端采用以集束调整（BA）为核心的图优化方法对位姿和路标进行优化。最终实验结果表明该系统能够满足实时性要求，并可以获得更加准确的位姿估计。

关键词: 同时定位与地图构建, 感知歧义, ORB, 闭环检测, 位姿估计

Abstract: Aiming at the problem that maps may be not consistent caused by accumulation of errors in visual Simultaneous Location and Mapping (SLAM), a Visual SLAM (V-SLAM) system based on improved closed-loop detection algorithm was proposed. To reduce the cumulative error caused by long operation of mobile robots, an improved closed-loop detection algorithm was introduced. By improving the similarity score function, the perceived ambiguity was reduced and finally the closed-loop recognition rate was improved. At the same time, to reduce the computational complexity, the environment image and depth information were directly obtained by Kinect, and feature extraction and matching was carried out by using small and robust ORB (Oriented FAST and Rotated BRIEF) features. RANdom SAmple Consensus (RANSAC) algorithm was used to delete mismatching pairs to obtain more accurate matching pairs, and then the camera poses were calculated by PnP. More stable and accurate initial estimation poses are critical to back-end processing, which were attained by g2o to carry on unstructured iterative optimization for camera poses. Finally, in the back-end Bundle Adjustment (BA) was used as the core of the map optimization method to optimize poses and road signs. The experimental results show that the system can meet the real-time requirements, and can obtain more accurate pose estimation.

Key words: Simultaneous Location And Mapping (SLAM), perceived ambiguity, Oriented FAST and Rotated BRIEF (ORB), closed-loop detection, pose estimation

中图分类号:

TP242.6

胡章芳, 鲍合章, 陈旭, 范霆铠, 赵立明. 基于改进闭环检测算法的视觉同时定位与地图构建[J]. 计算机应用, 2018, 38(3): 873-878.

HU Zhangfang, BAO Hezhang, CHEN Xu, FAN Tingkai, ZHAO Liming. Visual simultaneous location and mapping based on improved closed-loop detection algorithm[J]. Journal of Computer Applications, 2018, 38(3): 873-878.

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基于改进闭环检测算法的视觉同时定位与地图构建

Visual simultaneous location and mapping based on improved closed-loop detection algorithm

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