基于在线建立与匹配压缩全景路标的增强型视觉里程计

Lu Wei; Xiang Zhi-yu; Liu Ji-lin

doi:10.1631/FITEE.1400139

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基于在线建立与匹配压缩全景路标的增强型视觉里程计

计算机与自动化 | Updated：2022-06-06

- 基于在线建立与匹配压缩全景路标的增强型视觉里程计
- Design of an enhanced visual odometry by building and matching compressive panoramic landmarks online*
- 信息与电子工程前沿（英文） 2015年16卷第2期页码：152-165
- Affiliations：
  
  1.Institute of Information and Communication Engineering, Zhejiang University, Hangzhou 310027, China
  2.Zhejiang Provincial Key Laboratory of Information Network Technology, Hangzhou 310027, China
- Author bio：
  
  †E-mail:lwhfh01@zju.edu.cnxiangzy@zju.edu.cn
  ‡Corresponding Author
- Funds：
  
  *Project supported by the National Natural Science Foundation of China (Nos. 61071219 and 90820306) and the Fundamental Research Funds for the Central Universities, China
- DOI：10.1631/FITEE.1400139
  中图分类号：
- 收稿：2014-04-20，
  
  修回：2015-01-06，
  
  录用：2014-11-24，
  
  纸质出版：2015-02
- Accepted：
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基于在线建立与匹配压缩全景路标的增强型视觉里程计[J]. 信息与电子工程前沿（英文）, 2015,16(2):152-165.

Lu Wei, Xiang Zhi-yu, Liu Ji-lin. Design of an enhanced visual odometry by building and matching compressive panoramic landmarks online*[J]. Frontiers of Information Technology & Electronic Engineering, 2015, 16(2): 152-165.
基于在线建立与匹配压缩全景路标的增强型视觉里程计[J]. 信息与电子工程前沿（英文）, 2015,16(2):152-165. DOI： 10.1631/FITEE.1400139.

Lu Wei, Xiang Zhi-yu, Liu Ji-lin. Design of an enhanced visual odometry by building and matching compressive panoramic landmarks online*[J]. Frontiers of Information Technology & Electronic Engineering, 2015, 16(2): 152-165. DOI： 10.1631/FITEE.1400139.

摘要

目的

高效精确定位是移动机器人智能导航的先决条件。传统视觉定位系统，如视觉里程计（VO）和同时定位与三维重建（SLAM）算法，存在两点不足：一是由累积定位误差引起的漂移问题，二是由光照变化和移动物体导致的错误运动估计结果。

创新

通过引入全景相机到传统双目VO系统，提出一种增强型VO，高效利用全景相机360˚视场角信息。（1）在线建立路口场景压缩全景路标库；（2）机器人以任意方向重新访问路标时，对定位结果进行全局校正；（3）当双目立体VO不能提供可靠定位信息时对航向角估计结果进行校正；（4）为高效利用信息量较多的全景图像，引入压缩感知概念并提出一种自适应压缩特征。

方法

首先，在压缩亮度特征基础上，增加压缩SURF特征提高其描述能力，通过分析特征区分度，使压缩特征可以根据具体图像特点自适应调节，最终构建自适应压缩特征（ACF

图2），该特征计算速度快（表3）、描述能力强（图6、7，表1），有效提高全景图像信息利用效率。然后，使用ACF对全景路标图像进行描述，提出一种任意方向的路标图像匹配算法，若当前全景图像与路标图像匹配成功，则对当前定位结果进行全局位姿校正（图4），抑制大范围环境中定位路径漂移问题（图10、11）。最后，介绍基于图像片匹配的航向角鲁棒估计方法，当双目视觉里程计因特征跟踪质量差而导致运动估计结果不稳定时，对局部运动估计结果进行校正，提高运动估计的精度（图9）。

结论

提出的增强型视觉里程计系统可以准实时提供可靠定位结果，极大抑制大范围挑战性环境中传统VO漂移问题和运动估计错误问题。实验结果显示，所提算法大幅度提高传统VO的准确性和鲁棒性。

Abstract

Efficient and precise localization is a prerequisite for the intelligent navigation of mobile robots. Traditional visual localization systems

such as visual odometry (VO) and simultaneous localization and mapping (SLAM)

suffer from two shortcomings: a drift problem caused by accumulated localization error

and erroneous motion estimation due to illumination variation and moving objects. In this paper

we propose an enhanced VO by introducing a panoramic camera into the traditional stereo-only VO system. Benefiting from the 360° field of view

the panoramic camera is responsible for three tasks: (1) detecting road junctions and building a landmark library online; (2) correcting the robot’s position when the landmarks are revisited with any orientation; (3) working as a panoramic compass when the stereo VO cannot provide reliable positioning results. To use the large-sized panoramic images efficiently

the concept of compressed sensing is introduced into the solution and an adaptive compressive feature is presented. Combined with our previous two-stage local binocular bundle adjustment (TLBBA) stereo VO

the new system can obtain reliable positioning results in quasi-real time. Experimental results of challenging long-range tests show that our enhanced VO is much more accurate and robust than the traditional VO

thanks to the compressive panoramic landmarks built online.

关键词

Keywords

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