| 摘要: |
| 针对视觉SLAM闭环检测过程中由于感知歧义导致的闭环不准确问题，基于TF IDF方法提出了一种带权重的计算两幅图像相似性得分的算法，用于视觉SLAM的闭环检测研究;首先在特征点检测时，为了得到均匀分布且重叠点较少的特征点，使用FAST角点检测方法得到关键点，而后对关键点进行非极大值抑制；其次使用改进的算法计算两幅图像间带权重的相似性得分；最后根据场景图像的特征，进行闭环确认，进一步剔除错误闭环。实验通过搭建平台和使用标准数据集进行测试，证明了改进的闭环检测方法能够有效提高闭环的识别率与准确率。 |
| 关键词: 闭环检测 ORB TF-IDF熵 相似性得分函数 |
| DOI： |
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| 基金项目: |
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| An Improved Closed Loop Detection Algorithm Based on TF-IDF and ORB |
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RONG Gui-lan, XU Gang，XING Guang-xin
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Key Laboratory of Detection Technology and Energy Saving Devices of Anhui Province, Anhui University of Technology,Anhui Wuhu 241000, China
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| Abstract: |
| Aiming at the closed loop inaccuracy caused by perceptual ambiguity in the closed loop detection process of visual SLAM, a weighted algorithm for calculating the similarity scores of two images is proposed based on TF-IDF method, which is used for closed loop detection of visual SLAM. First, in the feature point detection, in order to obtain feature points that are evenly distributed and have fewer overlapping points, the FAST corner point detection method is used to obtain the key points, and then the key points are subjected to non-maximum suppression. Secondly, an improved algorithm is used to calculate the weighted similarity score between the two images. Finally, according to the characteristics of the scene image, closed loop confirmation is performed to further eliminate the error closed loop. The experiment tests the platform and uses the standard data set to prove that the improved closed-loop detection method can effectively improve the recognition rate and accuracy of the closed loop. |
| Key words: closed loop detection ORB TF IDF entropy similarity score function |
