基于ORB特征的改进视觉里程计算法

An Improved Visual Odometer Algorithm Based on ORB Features

针对VSLAM视觉里程计中图像特征点匹配信息冗余、特征点误匹配率高导致里程计误差大的问题,提出一种基于ORB特征的改进视觉里程计算法。首先,针对在关键点提取过程中提取效率低和关键点分布不合理的问题,提出对图像进行模块分割,利用灰度变化率和子区域特征有效因子剔除低质量图像模块和关键点的方法。其次,针对在两帧图像匹配过程中对过多无效特征点进行匹配产生误匹配的问题,提出利用相机前一时刻位姿剔除当前帧图像中不在前一帧图像视野中的特征点。实验结果表明,改进后的视觉里程计算法较原始的算法在相机位姿估计中具有更高的精度。

An improved visual odometer algorithm based on ORB features is proposed for resolving the problem of large odometer errors due to the matching information redundancy of image feature points and high mismatching rate of the feature points in the Visual Simultaneous Localization and Mapping(VSLAM) visual odometer.First,this paper suggests module decomposition for the images and deletion of low-quality image modules and key points by using the gray scale change rate and the efficiency factor of subregion features,so as to resolve the problem of low extraction efficiency in the course of key point extraction and unreasonable distribution of the key points.As for the mismatching of excessive invalid feature points in the process of two-frame image matching,this paper proposes a method of deleting the feature points that are not in the image view of last frame in the image of current frame by using the camera pose of last moment.Test results show that the improved visual odometer algorithm has higher precision in camera pose estimation compared with the previous one.