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학술대회자료
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제어로봇시스템학회 제어로봇시스템학회 국제학술대회 논문집 ICCAS 2023
발행연도
2023.10
수록면
172 - 177 (6page)

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초록· 키워드

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This study deals with the problem of visual loop closure detection that occurs when there are limited camera field of views. The proposed method generates a panoramic image using multiple images and creates sub-panorama images through the sliding window process. Then, a learning-based descriptor is formed for robust feature detection even in illumination changes and challenging environments. After that, the index of the sub-panorama image descriptor, which is closest to the query image descriptor, is found, and the final candidate is found which has the same index with the image of the raw database. Histogram equalization and image masking were conducted to create a robust panorama image before examining our proposed method. To prove the effectiveness of our proposed algorithm, a dataset trajectory was acquired in which visual loop detection was difficult. In addition, datasets were obtained indoors and outdoors to check whether the proposed algorithm is applicable to various environments. The acquired dataset is shared through the GitHub page at https://github.com/SungJaeShin/KAIST-DP.

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Abstract
1. INTRODUCTION AND RELATEDWORKS
2. VISUAL LOOP CLOSURE DETECTION
3. EXPERIMENTS
4. RESULTS
5. CONCLUSION AND FUTUREWORKS
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