文献类型：会议论文

英文题名：Vision localization method for intelligent vehicles in low-texture environments

作者：Liu, Zijian[1]; Zhang, Bingfeng[2]; Xu, Cheng[1]; Liu, Yuansheng[2]; Zhang, Jun[2]

第一作者：Liu, Zijian

机构：[1] Beijing Union University College of Robotics, Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing, 100101, China; [2] College of Robotics, Beijing Union University, Beijing, 100101, China

第一机构：北京联合大学北京市信息服务工程重点实验室

通讯机构：[2]College of Robotics, Beijing Union University, Beijing, 100101, China|[1141739]北京联合大学机器人学院;[11417]北京联合大学;

会议论文集：Proceedings - 2022 18th International Conference on Computational Intelligence and Security, CIS 2022

会议日期：December 16, 2022 - December 18, 2022

会议地点：Chengdu, China

语种：英文

外文关键词：Autonomous vehicles - Feature extraction - Intelligent robots - Textures

摘要：In order to address the problem that the intelligent vehicle will generate localization error due to the inability to extract enough feature points in the image under the low-texture environment, a detector-free method is proposed to accurately solve the intelligent vehicle position pose. Firstly, we perform dense matching at pixel level based on the improved LoFTR method for low-texture surfaces; secondly, we use DBSCAN to cluster the matching and select regions with higher matching confidence; finally, we use RANSAC to filter the outliers within the clustered regions to obtain accurate positional results. The experimental results on the public dataset show that the proposed method has good localization results. ? 2022 IEEE.