Loop closure detection based on image semantic feature and bag-of-words.

Autor: Sun, Hao, Wang, Peng, Ni, Cui, Li, Jin ming
Zdroj: Multimedia Tools & Applications; Apr2024, Vol. 83 Issue 12, p36377-36398, 22p
Abstrakt: Loop closure detection is a key component of visual SLAM(Simultaneous Localization and Mapping). However, the existing loop closure detection algorithms are easily affected by the illumination change and object change of the scene. Since semantic features of images can improve the accuracy of object location recognition, a loop closure detection algorithm based on image semantic features and bag-of-words model is proposed in this paper. Because of the evenly distributed image features can better reflect the content of the image. So firstly, the ORB feature extraction algorithm is improved to make the extracted feature points more evenly distributed in the image, and then the extracted feature points are used to build the bag-of-words model. Then the L2 norm is adopted to calculate the similarity between images, and according to which the loop closure candidate images are determined quickly. In order to reduce the adverse effects of illumination changes and object changes on loop closure detection, YOLOv4 is used to extract semantic features of images in this paper, and real loop closure will be screened from the candidate images according to cosine values of included angles between similar objects in different images, so as to complete the loop closure detection. Experiments on TUM dataset and actual images show that the proposed algorithm can effectively reduce the adverse effects of illumination changes and object changes on loop closure detection, and effectively improve the accuracy and adaptability of loop closure detection. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index
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