基于改进ORB特征的单目视觉SLAM算法

MONOCULAR VISUAL SLAM ALGORITHM BASED ON IMPROVED ORB FEATURE

单目视觉SLAM算法容易受场景纹理影响出现初始化失败或者相机轨迹漂移的问题。为此,提出一种基于改进ORB特征的单目视觉SLAM算法。对输入视频帧构建高斯金字塔提取FAST特征,综合考虑特征点的灰度信息与其邻域的梯度信息生成描述子,并采用多网格策略划分特征点邻域,凭借改进的特征点降低运动模型算法的迭代次数,达到较好的时间效率。实验表明,该算法的相机轨迹精度与特征匹配精度相较于ORB-SLAM得到有效提高,并且能很好地适应多种纹理特殊场景。

Monocular visual SLAM algorithm is easily affected by scene texture,which leads to initialization failure,and camera trajectory drift.Aiming at the above problems,the monocular visual SLAM algorithm based on improved ORB features is proposed.Firstly,a Gaussian pyramid was constructed to extract FAST features for the input video frame.Then the gray information of the feature point and the gradient information of its neighborhood were comprehensively considered to generate descriptors,and the multi-grid strategy was used to divide the image patch.Finally,with the improved feature points,the number of iterations of the model algorithm was reduced,so that the time efficiency was better.The experiments show that the camera track accuracy and feature point accuracy of improved ORB-SLAM are increased compared with ORB-SLAM,and it can adapt well to multiple weak texture scenes.