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Proceedings Paper

Study on feature extraction algorithm of mobile robot vision SLAM under dynamic illumination
Author(s): Chunjie Hua; Yanan Yu; Zhongmin Wang
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Paper Abstract

When a feature point detection method is used in vision SLAM to match images, environment condition around the robot is uncertain usually. Many influence factors such as rotation, scale, fuzzy as well as illumination in the process of detection have a strong impact on robot's locating and incremental map building. Experiments proved that SIFT, SURF, BRISK, ORB and FREAK have good robustness under normal illumination. However, the illumination is complex in practical applications, and the stability of image features extraction will be affected. Based on a mobile robot vision platform, the speed, repetition rate and matching rate of five feature extraction algorithms above are compared and analyzed with different methods. Under dynamic illumination, the robustness and matching effect of image features with translation, rotation, scale and fuzzy transformations are also compared. Through experimental data analyzing, BRISK features shows better effect under dynamic illumination.

Paper Details

Date Published: 18 December 2019
PDF: 6 pages
Proc. SPIE 11338, AOPC 2019: Optical Sensing and Imaging Technology, 113381R (18 December 2019); doi: 10.1117/12.2544551
Show Author Affiliations
Chunjie Hua, Tianjin Univ. of Technology and Education (China)
Yanan Yu, Tianjin Univ. of Technology and Education (China)
Zhongmin Wang, Tianjin Univ. of Technology and Education (China)

Published in SPIE Proceedings Vol. 11338:
AOPC 2019: Optical Sensing and Imaging Technology
John E. Greivenkamp; Jun Tanida; Yadong Jiang; HaiMei Gong; Jin Lu; Dong Liu, Editor(s)

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