基于语义位置验证的鲁棒闭环检测算法
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作者单位:

1.浙江工业大学 信息工程学院;2.北京航空航天大学 自动化科学与电气工程学院

作者简介:

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中图分类号:

TP242

基金项目:

浙江省重点研发计划(2020C01109); 金华市重大(重点)科学技术研究计划项目(2021-1-012)


Robust loop closure detection algorithm based on semantic position verification
Author:
Affiliation:

1.College of Information Engineering,Zhejiang University of Technology;2.School of Automation Science and Electrical Engineering, Beihang University

Fund Project:

Key Research and Development Program of Zhejiang Province (No. 2020C01109); Jinhua Major (Key) Science and Technology Research Program Projects(2021-1-012)

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    摘要:

    针对现有闭环检测算法因视觉干扰而产生的闭环误判问题,本文提出一种利用场景语义信息进行验证的闭环检测算法.该算法通过视觉词袋库模型检索闭环候选帧,用后验方法剔除算法可能产生的错误闭环.后验方法首先将场景中的语义信息抽象为语义节点,然后提取包含邻域信息的节点特征,并以此匹配图像间的语义节点,降低了算法对于动态物体干扰的敏感性.随后,算法基于图像间的匹配语义节点构建相对位置网络,根据网络相似度验证闭环帧,提高了闭环检测算法在具有相似纹理的场景中应对感知混淆的鲁棒性.实验结果表明,语义位置验证算法显著地提升了视觉词袋模型的检测准确率.与其他经典算法相比,本文所提出的闭环检测算法具备更快速、更准确的检测性能.

    Abstract:

    To address the loop closure misjudgment problem due to visual interference, this paper proposes a loop closure detection algorithm that uses semantic information to verify candidates. The algorithm retrieves loop closure candidates using visual bag-of-words model, and applies a verification method to eliminate the mismatches. The proposed verification method first extract semantic nodes on the basis of semantic information in the scene, and then calculates the node descriptors containing neighboring information so that we can accurately match the semantic nodes across images, reducing the sensitivity of the algorithm to dynamic objects. Subsequently, the algorithm constructs a relative position network based on matched semantic nodes from two views, and verifies loop closure candidates based on the similarity of network, which improves the robustness of the algorithm against perceptual aliasing. Experimental results show that the semantic position verification method significantly improves the detection accuracy of the visual bag-of-words model. Compared with other classic algorithms, the overall loop closure detection algorithm has achieved leading performances in both detection accuracy and computational efficiency.

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  • 收稿日期:2021-12-30
  • 最后修改日期:2022-04-06
  • 录用日期:2022-04-08
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