Illumination robust image transformations for feature-based SLAM using photometric and feature matches loss

doi:10.19682/j.cnki.1005-8885.2022.1012

中国邮电高校学报(英文) ›› 2022, Vol. 29 ›› Issue (3): 92-104.doi: 10.19682/j.cnki.1005-8885.2022.1012

• Others • 上一篇

Illumination robust image transformations for feature-based SLAM using photometric and feature matches loss

School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China

收稿日期:2021-10-27 修回日期:2022-04-07 出版日期:2022-06-30 发布日期:2022-06-30
通讯作者: Yan Danfeng E-mail:yandf@bupt.edu.cn

Illumination robust image transformations for feature-based SLAM using photometric and feature matches loss

School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China

Received:2021-10-27 Revised:2022-04-07 Online:2022-06-30 Published:2022-06-30
Contact: Yan Danfeng E-mail:yandf@bupt.edu.cn

摘要/Abstract

摘要：

Simultaneous localization and mapping (SLAM) technology becomes more and more important in robot localization. The purpose of this paper is to improve the robustness of visual features to lighting changes and increase the recall rate of map re-localization under different lighting environments by optimizing the image transformation model. An image transformation method based on matches and photometric error (name the method as MPT) is proposed in this paper, and it is seamlessly integrated into the pre-processing stage of the feature-based visual SLAM framework. The results of the experiment show that the MPT method has a better matching effect on different visual features. In addition, the image transformation module encapsulated by a robot operating system (ROS) can be used with multiple visual SLAM systems and improve its re-localization effect under different lighting environments.

关键词: simultaneous localization and mapping (SLAM), image transformation, long-term visual localization

Abstract: Simultaneous localization and mapping (SLAM) technology becomes more and more important in robot localization. The purpose of this paper is to improve the robustness of visual features to lighting changes and increase the recall rate of map re-localization under different lighting environments by optimizing the image transformation model. An image transformation method based on matches and photometric error (name the method as MPT) is proposed in this paper, and it is seamlessly integrated into the pre-processing stage of the feature-based visual SLAM framework. The results of the experiment show that the MPT method has a better matching effect on different visual features. In addition, the image transformation module encapsulated by a robot operating system (ROS) can be used with multiple visual SLAM systems and improve its re-localization effect under different lighting environments.

Key words: simultaneous localization and mapping (SLAM), image transformation, long-term visual localization

Zhang Miao, Wang Zixian, Yan Danfeng. Illumination robust image transformations for feature-based SLAM using photometric and feature matches loss[J]. The Journal of China Universities of Posts and Telecommunications, 2022, 29(3): 92-104.

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Illumination robust image transformations for feature-based SLAM using photometric and feature matches loss

Illumination robust image transformations for feature-based SLAM using photometric and feature matches loss

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