ISSN (print) 0914-4935
ISSN (online) 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.

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Image-similarity-based Convolutional Neural Network for Robot Visual Relocalization

Li Wang, Ruifeng Li, Jingwen Sun, Hock Soon Seah, Chee Kwang Quah, Lijun Zhao, and Budianto Tandianus

(Received July 31, 2019; Accepted December 2, 2019)

Keywords: visual relocalization, CNN, image similarity

Convolutional neural network (CNN)-based methods, which train an end-to-end model to regress asix degree of freedom (DoF) pose of a robot from a single red-green-blue (RGB) image, have been developed to overcome the poor robustness of robot visual relocalization recently. However, the pose precision becomes low when the test image is quite dissimilar compared with training images. In this paper, we propose a novel method, named image-similarity-based CNN, which considers the image similarity of an input image during the CNN training. The higher the similarity of the input image, the better precision we can achieve. Therefore, we crop the input image into several small image blocks, and the similarity between each cropped image block and training dataset images is measured by employing a feature vector in a fully connected layer of CNN. Finally, the most similar image is selected to regress the pose. A genetic algorithm is utilized to determine the cropped position. Experiments on both open-source dataset 7-Scenes and two actual indoor environments are conducted. The results show that the proposed algorithm leads to better results and reduces large regression errors effectively compared with existing solutions.

Corresponding author: Ruifeng Li, Lijun Zhao




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