强照度鲁棒的SLAM算法

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,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,相比ORB-SLAM算法,在保证实时性不变的情况下,本文算法精度与光照强度鲁棒性有明显提升。

Illumination robust online loop SLAM algorithm

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.