Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/147613
Title: Image-similarity-based convolutional neural network for robot visual relocalization
Authors: Wang, Li
Li, Ruifeng
Sun, Jingwen
Seah, Hock Soon
Quah, Chee Kwang
Zhao, Lijun
Tandianus, Budianto
Keywords: Engineering::Computer science and engineering
Issue Date: 2020
Source: Wang, L., Li, R., Sun, J., Seah, H. S., Quah, C. K., Zhao, L. & Tandianus, B. (2020). Image-similarity-based convolutional neural network for robot visual relocalization. Sensors and Materials, 32(4), 1245-1259. https://dx.doi.org/10.18494/SAM.2020.2549
Journal: Sensors and Materials
Abstract: Convolutional neural network (CNN)-based methods, which train an end-to-end model to regress a six 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 dissimilar to 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 higher 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 CNN layer. 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.
URI: https://hdl.handle.net/10356/147613
ISSN: 0914-4935
DOI: 10.18494/SAM.2020.2549
Rights: © 2020 MYU K.K. This work is licensed under a Creative Commons Attribution 4.0 International License.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

Files in This Item:
File Description SizeFormat 
SM2174.pdf2.85 MBAdobe PDFView/Open

Page view(s)

12
Updated on May 8, 2021

Download(s)

1
Updated on May 8, 2021

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.