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孙柏,郭中华,郑果果. 强照度鲁棒的SLAM算法[J]. 科学技术与工程, 2019, 19(33): 266-271.
SUN Bo,ZHENG Guo-guo.Illumination robust online loop SLAM algorithm[J].Science Technology and Engineering,2019,19(33):266-271.
强照度鲁棒的SLAM算法
Illumination robust online loop SLAM algorithm
投稿时间:2019-03-07  修订日期:2019-08-16
DOI:
中文关键词:  光照强度鲁棒性  HSV空间  高斯混合模型  视觉字典  贝叶斯滤波模型
英文关键词:Illumination  robustness HSV  space Gauss  mixture model  Visual dictionary  Bayesian filtering  model
基金项目:国家自然科学基金资助项目(61565014);
        
作者单位
孙柏 宁夏大学物理与电子电气工程学院
郭中华 宁夏大学物理与电子电气工程学院
郑果果 宁夏大学物理与电子电气工程学院
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中文摘要:
      ORB-SLAM算法通过ORB(oriented FAST and rotated BRIEF)描述子匹配特征点,其光照强度鲁棒性不足,难以在光照条件较差时应用。对此,利用HSV空间中色调(Hue)光照强度鲁棒性较强的特点,本文提出通过高斯混合模型于前端匹配时将色调加入ORB特征匹配的方法,以解决特征匹配时光照强度鲁棒性不足的问题。通过光束平差法(Bundle Adjustment)进行位姿优化后,基于贝叶斯滤波模型,根据当前场景构建视觉字典以完成回环检测,提高SLAM算法精度。实验结果表明,本文算法相比ORB-SLAM算法,在保证实时性不变的情况下,精度与光照强度鲁棒性有明显提升。
英文摘要:
      ORB-SLAM describes sub-matching feature points by ORB (oriented FAST and rotated BRIEF). Its illumination intensity robustness is insufficient and it is difficult to apply in poor illumination conditions. For this reason, taking advantage of the strong robustness of hue illumination intensity in HSV space, a method of adding hue to ORB feature matching in front-end matching based on Gauss mixture model is proposed to solve the problem of insufficient robustness of illumination intensity in feature matching. Based on Bayesian filtering model, a visual dictionary is constructed according to the current scene to complete loop detection and improve the accuracy of SLAM algorithm after position and attitude optimization by Bundle Adjustment. The experimental results show that, compared with ORB-SLAM algorithm, the accuracy and robustness of illumination intensity of the proposed algorithm are significantly improved under the condition of guaranteeing the real-time performance unchanged.
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