北京航空航天大学学报 ›› 2021, Vol. 47 ›› Issue (1): 24-30.doi: 10.13700/j.bh.1001-5965.2019.0642

• 论文 • 上一篇    下一篇

一种高效准确的视觉SLAM闭环检测算法

安平, 王国平, 余佳东, 陈亦雷, 尤志翔   

  1. 上海大学 通信与信息工程学院, 上海 200444
  • 收稿日期:2019-12-23 发布日期:2021-01-29
  • 通讯作者: 安平 E-mail:anping@shu.edu.cn
  • 作者简介:安平,女,博士,教授,博士生导师。主要研究方向:图像与视频处理、计算机视觉;王国平,男,硕士研究生。主要研究方向:视觉SLAM;余佳东,男,硕士研究生。主要研究方向:视觉SLAM。
  • 基金资助:
    国家自然科学基金(61828105);上海市科委项目(17DZ2292400,18XD1423900)

An efficient and accurate visual SLAM loop closure detection algorithm

AN Ping, WANG Guoping, YU Jiadong, CHEN Yilei, YOU Zhixiang   

  1. School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
  • Received:2019-12-23 Published:2021-01-29

摘要: 同步定位与地图构建(SLAM)是视觉导航领域的关键技术之一,闭环检测是SLAM的基础问题。针对视觉SLAM闭环检测准确率不高的问题,提出一种高效准确的闭环检测算法。该算法由词袋模型、图像结构校验、跟踪预测模型3个模块构成。首先,将局部特征与全局特征相结合,设计了词袋模型与图像结构校验模块。词袋模型通过视觉单词比较图像之间的相似性,得到闭环候选帧。然后,图像结构校验模块灰度化、归一化当前图像与闭环候选图像。归一化之后的图像被直接作为局部特征的邻域,计算得到全局描述符,通过全局描述符判断闭环候选帧是否为有效的闭环。最后,针对传统闭环检测算法耗时随图像数量增加而显著增加的问题,设计了跟踪预测模块,以提高计算效率。实验中,与主流的DBoW算法相比,提出的闭环检测算法的准确率提升了20%以上,实时性也有更好的表现。

关键词: 同步定位与地图构建(SLAM), 闭环检测, 局部特征, 全局特征, 跟踪预测

Abstract: Simultaneous Localization and Mapping (SLAM) is one of the key technologies in visual navigation, and loop closure detection is a basis of SLAM. An efficient and accurate loop closure detection algorithm is proposed to solve the problem of low accuracy rate of SLAM loop closure detection. The loop closure detection algorithm consists of bag of words module, structure checking module, and tracking module. First, we design the bag of words model and structure checking module, combining local features with holistic features. The bag of words model compares the image similarities using visual words to obtain the closed-loop candidate frame. Then, structure checking module grayscales and normalizes the current image and the closed-loop candidate image. The normalized images are directly used as the patch of local feature to obtain holistic feature. Whether the closed-loop candidate frame is a valid closed loop is determined by the holistic descriptor. To address the problem that time consumption increases rapidly with the increase of image numbers, we design the tracking module to improve the computational efficiency. The comparative experiments with DBoW algorithm show that the proposed algorithm improves the accuracy by more than 20% and also has better real-time performance.

Key words: Simultaneous Localization and Mapping (SLAM), loop closure detection, local features, holistic features, track and predict

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