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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: |
| 基于点线综合特征的视觉SLAM闭环检测算法 柳长安，程瑞营，赵丽娟 （华北电力大学，控制与计算机工程学院，北京 102206） 摘要：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混淆，且当场景缺乏纹理信息时可提取的点特征数量很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。本文对采集图像分别提取点特征和线特征，并构建一种可以融合点线特征的混合特征词典树，建立可以表征混合特征的单词向量，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。最后通过实验验证了本文所提出算法的可行性和有效性。 关键词：闭环检测；即时定位与地图构建；视觉单词；点线综合 |
