改进单目视觉里程计的位姿图

2014 19th International Conference on Methods and Models in Automation and Robotics (MMAR) Pub Date : 2014-11-20 DOI:10.1109/MMAR.2014.6957413

P. Kicman, J. Narkiewicz

{"title":"改进单目视觉里程计的位姿图","authors":"P. Kicman, J. Narkiewicz","doi":"10.1109/MMAR.2014.6957413","DOIUrl":null,"url":null,"abstract":"In this paper the monocular visual odometry algorithm augmented with pose graph optimization is presented. The algorithm was tested using five different combinations of feature extractors and descriptors and evaluated using two challenging datasets from KITTI database. The main result of this study is that the implementation of pose graph optimization may lead to reduction of position error ranging between 1.53% to 76.05%. The error reduction depends on a feature type and dataset used.","PeriodicalId":166287,"journal":{"name":"2014 19th International Conference on Methods and Models in Automation and Robotics (MMAR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Pose graph for improved monocular visual odometry\",\"authors\":\"P. Kicman, J. Narkiewicz\",\"doi\":\"10.1109/MMAR.2014.6957413\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper the monocular visual odometry algorithm augmented with pose graph optimization is presented. The algorithm was tested using five different combinations of feature extractors and descriptors and evaluated using two challenging datasets from KITTI database. The main result of this study is that the implementation of pose graph optimization may lead to reduction of position error ranging between 1.53% to 76.05%. The error reduction depends on a feature type and dataset used.\",\"PeriodicalId\":166287,\"journal\":{\"name\":\"2014 19th International Conference on Methods and Models in Automation and Robotics (MMAR)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-11-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 19th International Conference on Methods and Models in Automation and Robotics (MMAR)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MMAR.2014.6957413\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 19th International Conference on Methods and Models in Automation and Robotics (MMAR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MMAR.2014.6957413","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 0

摘要

本文提出了一种增强位姿图优化的单目视觉测程算法。该算法使用五种不同的特征提取器和描述符组合进行了测试，并使用KITTI数据库的两个具有挑战性的数据集进行了评估。本研究的主要结果是，位姿图优化的实施可使位置误差降低1.53% ~ 76.05%。减少误差取决于特征类型和使用的数据集。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Pose graph for improved monocular visual odometry

In this paper the monocular visual odometry algorithm augmented with pose graph optimization is presented. The algorithm was tested using five different combinations of feature extractors and descriptors and evaluated using two challenging datasets from KITTI database. The main result of this study is that the implementation of pose graph optimization may lead to reduction of position error ranging between 1.53% to 76.05%. The error reduction depends on a feature type and dataset used.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

2014 19th International Conference on Methods and Models in Automation and Robotics (MMAR)

自引率

0.00%

发文量