Data Association Using Visual Object Recognition for EKF-SLAM in Home Environment

2006 IEEE/RSJ International Conference on Intelligent Robots and Systems Pub Date : 2006-10-01 DOI:10.1109/IROS.2006.281936

SungHwan Ahn, Minyong Choi, Jinwoo Choi, W. Chung

{"title":"Data Association Using Visual Object Recognition for EKF-SLAM in Home Environment","authors":"SungHwan Ahn, Minyong Choi, Jinwoo Choi, W. Chung","doi":"10.1109/IROS.2006.281936","DOIUrl":null,"url":null,"abstract":"Reliable data association is crucial to localization and map building for mobile robot applications. For that reason, many mobile robots tend to choose vision-based SLAM solutions. In this paper, a SLAM scheme based on visual object recognition, not just a scene matching, in home environment is proposed without using artificial landmarks. For the object-based SLAM, the following algorithms are suggested: 1) a novel local invariant feature extraction by combining advantages of multi-scale Harris corner as a detector and its SIFT descriptor for natural object recognition, 2) the RANSAC clustering for robust object recognition in the presence of outliers and 3) calculating accurate metric information for SLAM update. The proposed algorithms increase robustness by correct data association and accurate observation. Moreover, it also can be easily implemented real-time by reducing the number of representative landmarks, i.e. objects. The performance of the proposed algorithm was verified by experiments using EKF-SLAM with a stereo camera in home-like environments, and it showed that the final pose error was bounded after battery-run-out autonomous navigation for 50 minutes","PeriodicalId":237562,"journal":{"name":"2006 IEEE/RSJ International Conference on Intelligent Robots and Systems","volume":"36 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"49","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 IEEE/RSJ International Conference on Intelligent Robots and Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IROS.2006.281936","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 49

Abstract

Reliable data association is crucial to localization and map building for mobile robot applications. For that reason, many mobile robots tend to choose vision-based SLAM solutions. In this paper, a SLAM scheme based on visual object recognition, not just a scene matching, in home environment is proposed without using artificial landmarks. For the object-based SLAM, the following algorithms are suggested: 1) a novel local invariant feature extraction by combining advantages of multi-scale Harris corner as a detector and its SIFT descriptor for natural object recognition, 2) the RANSAC clustering for robust object recognition in the presence of outliers and 3) calculating accurate metric information for SLAM update. The proposed algorithms increase robustness by correct data association and accurate observation. Moreover, it also can be easily implemented real-time by reducing the number of representative landmarks, i.e. objects. The performance of the proposed algorithm was verified by experiments using EKF-SLAM with a stereo camera in home-like environments, and it showed that the final pose error was bounded after battery-run-out autonomous navigation for 50 minutes

查看原文本刊更多论文

基于视觉目标识别的EKF-SLAM数据关联

可靠的数据关联对于移动机器人应用的定位和地图构建至关重要。因此，许多移动机器人倾向于选择基于视觉的SLAM解决方案。本文提出了一种不使用人工地标的基于视觉目标识别而不仅仅是场景匹配的家居环境SLAM方案。对于基于目标的SLAM，提出了以下算法:1)结合多尺度Harris角点作为检测器的优势及其SIFT描述子的局部不变特征提取算法，用于自然目标识别;2)RANSAC聚类算法，用于异常值存在下的鲁棒目标识别;3)计算准确的度量信息，用于SLAM更新。该算法通过正确的数据关联和准确的观测，增强了鲁棒性。此外，它还可以通过减少代表性地标(即物体)的数量来轻松实现实时。利用EKF-SLAM与立体相机在家居环境下的实验验证了该算法的性能，结果表明，电池耗尽自主导航50分钟后，最终姿态误差是有边界的

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

2006 IEEE/RSJ International Conference on Intelligent Robots and Systems

自引率

0.00%

发文量