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FITEE
Front. Inform. Technol. Electron. Eng.  2015, Vol. 16 Issue (2): 152-165    DOI: 10.1631/FITEE.1400139
    
基于在线建立与匹配压缩全景路标的增强型视觉里程计
Wei Lu, Zhi-yu Xiang, Ji-lin Liu
Institute of Information and Communication Engineering, Zhejiang University, Hangzhou 310027, China; Zhejiang Provincial Key Laboratory of Information Network Technology, Hangzhou 310027, China
Design of an enhanced visual odometry by building and matching compressive panoramic landmarks online
Wei Lu, Zhi-yu Xiang, Ji-lin Liu
Institute of Information and Communication Engineering, Zhejiang University, Hangzhou 310027, China; Zhejiang Provincial Key Laboratory of Information Network Technology, Hangzhou 310027, China
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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。
关键词： 视觉里程计;  全景路标;  路标匹配;  压缩感知;  自适应压缩特征    
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.
Key words: Visual odometry    Panoramic landmark    Landmark matching    Compressed sensing    Adaptive compressive feature
收稿日期: 2014-04-20 出版日期: 2015-01-29
CLC:  TP391  
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Wei Lu, Zhi-yu Xiang, Ji-lin Liu. Design of an enhanced visual odometry by building and matching compressive panoramic landmarks online. Front. Inform. Technol. Electron. Eng., 2015, 16(2): 152-165.

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http://www.zjujournals.com/xueshu/fitee/CN/10.1631/FITEE.1400139    或    http://www.zjujournals.com/xueshu/fitee/CN/Y2015/V16/I2/152

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