李永锋, 张国良, 王蜂, 汤文俊, 姚二亮. 一种基于历史模型集的改进闭环检测算法[J]. 机器人, 2015, 37(6): 663-673. DOI: 10.13973/j.cnki.robot.2015.0663
引用本文: 李永锋, 张国良, 王蜂, 汤文俊, 姚二亮. 一种基于历史模型集的改进闭环检测算法[J]. 机器人, 2015, 37(6): 663-673. DOI: 10.13973/j.cnki.robot.2015.0663
LI Yongfeng, ZHANG Guoliang, WANG Feng, TANG Wenjun, YAO Erliang. An Improved Loop Closure Detection Algorithm Based on Historical Model Set[J]. ROBOT, 2015, 37(6): 663-673. DOI: 10.13973/j.cnki.robot.2015.0663
Citation: LI Yongfeng, ZHANG Guoliang, WANG Feng, TANG Wenjun, YAO Erliang. An Improved Loop Closure Detection Algorithm Based on Historical Model Set[J]. ROBOT, 2015, 37(6): 663-673. DOI: 10.13973/j.cnki.robot.2015.0663

一种基于历史模型集的改进闭环检测算法

An Improved Loop Closure Detection Algorithm Based on Historical Model Set

  • 摘要: 为了提高移动机器人同时定位与地图创建(SLAM)问题中闭环检测的准确率和实时性,提出了一种基于历史模型集的改进闭环检测算法.首先,在基于Kinect传感器的帧到模型配准模型的基础上,增加特征描述向量并使用加权方法对其进行更新,从而构建历史模型集,并利用视觉词典树(visual vocabulary tree)对历史模型集和当前帧数据进行场景描述;其次,以反比例函数代替最小值函数,使两幅图像在单个节点的相似性得分函数得以优化,从而得到改进的金字塔TF-IDF(词频-逆向文件频率)得分匹配方法.一方面,改进方法能够减少感知歧义,提高闭环检测的准确率;另一方面,利用改进方法对当前帧数据与历史模型集的从属关系进行有效判断,与传统逐帧比较方法相比,比较次数明显减少,闭环检测的实时性得到较大提高.再次,使用改进的金字塔TF-IDF得分匹配方法对当前帧数据和候选历史模型集所包含的关键帧进行相似性分析,进而提取候选闭环;最后,从时间连续性和对极几何约束两个方面剔除误正闭环.数据集和实际场景对比实验均表明,相比于IAB-MAP(incremental appearance-based mapping)、FAB-MAP(fast appearance-based mapping)和RTAB-MAP(real-time appearance-based mapping),本文的闭环检测算法具有更好的实时性,且在确保100%准确率的情况下,具有更高的召回率.

     

    Abstract: An improved loop closure detection algorithm based on historical model set is proposed to improve the precision ratio and real-time performance of loop closure detection in mobile robot SLAM(simultaneous localization and mapping). First of all, the descriptor of feature is added to the model on the basis of frame-to-model registration model based on Kinect sensor, and it is updated with a weighting method to construct historical model set. A visual vocabulary tree is used for scene description of the historical model set and current frame. Then the pyramid TF-IDF(term frequency-inverse document frequency) scoring match scheme is improved by using inverse proportional function instead of minimum function to optimize the similarity score at a single node between two images. On one hand, the perceptual aliasing can be reduced with the improved scoring match scheme, and thus the precision ratio of loop closure detection is increased. On the other hand, by using the improved scoring match scheme, the affiliation between current frame and historical model set can be judged effectively with fewer number of comparison than the traditional frame-by-frame comparison method, so that the real-time performance of loop closure detection is improved. Next, the improved pyramid TF-IDF scoring match scheme is utilized for the similarity analysis between current frame and the keyframes affiliated to the candidate historical model set to extract candidate loop closure. Finally, the outliers in the candidate loop closure are discarded according to temporal continuity and epipolar geometry constraints. Contrast experiments based on the datasets and the actual scene show that, compared with the IAB-MAP(incremental appearance-based mapping), FAB-MAP(fast appearance-based mapping) and RTAB-MAP(real-time appearance-based mapping), the improved loop closure detection algorithm proposed has better real-time performance and higher recall ratio in the case of 100% precision ratio.

     

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