基于室内标志的视觉定位方法

黄刚; 蔡浩; 邓超; 何志; 许宁波

基于室内标志的视觉定位方法

黄刚¹,
蔡浩^2, ,,
邓超¹,
何志¹,
许宁波¹

1. 武汉科技大学汽车与交通工程学院武汉 430081;
2. 武汉纺织大学计算机与人工智能学院武汉 43020

基金项目:

国家自然科学基金青年基金项目（52002298）、湖北省自然科学基金青年项目（2020CFB118）、湖北省教育厅科学技术研究计划青年人才项目（Q20201107）资助

详细信息

作者简介:
黄刚(1989-),博士,讲师.研究方向:室内定位、智能汽车感知、场景建模.E-mail:ghuang@wust.edu.cn

通讯作者:
蔡浩(1989-),博士,讲师.研究方向:智能交通系统、安全辅助驾驶、智能车定位、驾驶行为分析.E-mail:caihao@wtu.edu.cn

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出版历程
- 收稿日期: 2020-05-23
- 网络出版日期: 2021-12-14

Indoor Sign-based Visual Localization Method

HUANG Gang¹,
CAI Hao^{2
, ,},
DENG Chao¹,
HE Zhi¹,
XU Ningbo¹

1. School of Automobile and Traffic Engineering, Wuhan University of Science and Technology, Wuhan 430081, China;
2. School of Computer Science and Artificial Intelligence, Wuhan Textile University, Wuhan 430200, China

摘要

摘要: 为解决室内交通场景中智能汽车和移动机器人进行定位计算的问题，利用室内场景中已存在的各类标志，引入BEBLID（Boosted Efficient Binary Local Image Descriptor）算法，提出1种视觉定位方法。对BEBLID算法进行改进，赋予其对图像整体进行特征表征的能力。将定位过程分解为离线阶段和在线阶段，离线阶段构建场景标志地图，在线阶段将当前图像所提取的全局和局部BEBLID特征与场景标志地图的对应特征进行匹配，引入KNN方法确定最近节点和最近图像，并利用场景特征地图中存储的标志坐标信息，进行度量计算，获取当前位置信息。在教学楼、办公楼和室内停车场场景进行实验，实验中对场景标志的正确识别率达到90%，平均定位误差小于1 m，与传统方法相比，同一样本下识别精度相对提升约10%，实验验证了算法的有效性。
- 室内定位 /
- 全局特征 /
- 视觉定位 /
- BEBLID特征
Abstract: To solve the problem of localization calculation of intelligent vehicles and the mobile robot in the indoor traffic environment, by exploiting kinds of signs which existed in the indoor environment, a visual localization method is proposed through using BEBLID (Boosted Efficient Binary Local Image Descriptor) algorithm. The proposed method enforces the ability to characterize the whole image by improving the classic BEBLID. In this paper, the localization method consists of an offline stage and an online stage. In the offline stage, a scene sign map is created. In the online stage, the calculation progress is divided into 3 parts, which include holistic and local BEBLID method from current image and image in the scene sign map, closet sign site and closet image calculation by using KNN method, metric calculation by using coordinate information which is stored in the scene sign map. The experiment is conducted in three kinds of indoor scenes, including a teaching building, an office building, and an indoor parking lot. The experiment shows the scene sign recognition rate reached 90%, and the average localization error is less than 1 meter. Compared with the traditional method, the proposed method improves about 10% relative recognition rate with the same test set, which verified the effectiveness of the proposed method.
- Indoor localization /
- Holistic feature /
- Visual localization /
- BEBLID feature

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参考文献(20)

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