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| Abstract: |
| For traditional loop closure detection algorithm, only using the vectorization of point features to build visual dictionary is likely to cause perceptual ambiguity. In addition, when scene lacks texture information, the number of point features extracted from it will be small and cannot describe the image effectively. Therefore, this paper proposes a loop closure detection algorithm which combines point and line features. To better recognize scenes with hybrid features, the building process of traditional dictionary tree is improved in the paper. The features with different flag bits were clustered separately to construct a mixed dictionary tree and word vectors that can represent the hybrid features, which can better describe structure and texture information of scene. To ensure that the similarity score between images is more reasonable, different similarity coefficients were set in different scenes, and the candidate frame with the highest similarity score was selected as the candidate closed loop. Experiments show that the point-line comprehensive feature was superior to the single feature in the structured scene and the strong texture scene, the recall rate of the proposed algorithm was higher than the state-of-the-art methods when the accuracy is 100%, and the algorithm can be applied to more diverse environments. |
| Key words: loop closure detection SLAM visual dictionary point and line features |
| DOI：10.11916/j.issn.1005-9113.18123 |
| Clc Number:TP242 |
| Fund: |
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| Descriptions in Chinese: |
| 针对传统闭环检测算法中对单一的点特征矢量化构建视觉词典容易引起感知歧义，以及对于缺乏纹理信息的场景可提取的点特征数量较少不能有效的描述图像等问题，本文提出一种基于点线综合特征的闭环检测算法。为了融合多种特征更好的进行场景识别，本文对传统的词典树建立过程进行了改进。在预处理时对提取到的图像的点、线特征设置不同标志位，对含不同标志位的特征分开聚类，构建混合词典树并建立可以表征混合特征的单词向量，从而更好地描述环境的结构化信息和纹理信息。为使图像间相似度得分更合理，本文在不同的环境下设置不同相似度系数，选取相似性得分最高的候选帧作为候选闭环。实验表明，点线综合特征优于结构化场景中的单一特征，在100%准确率的情况下，该方法可应用于多种环境，召回率高于最先进的方法。 |
