基于RGB-D相机数据的SLAM算法研究

Research on SLAM Algorithm Based on RGB-D Camera Data

提出了一种基于RGB-D相机数据的同时定位与地图构建(SLAM)算法系统,实现对RGB-D数据的快速和准确构建.首先在RGB图中提取较好鲁棒性的SURF特征,结合使用快速最近邻近似(FLANN)来完成特征点匹配的方式,再使用基于改进的最小距离与随机采样一致性(RANSAC)组合的方法剔除误匹配,然后使用PNP求解相邻帧间的相机位姿变换关系,后端的优化使用G2O(general graph optimization)来优化全局位姿,并使用回环检测消除累计误差.实验证明该方法具有有效性和可行性,能够迅速、准确地构建出三维稠密地图.

A simultaneous localization and mapping(SLAM)algorithm system based on RGB-D camera data is proposed to achieve rapid and accurate construction of threedimensional dense map.Firstly,SURF features is extracted with good robustness from the RGB image,and Fast Library for Approximate Nearest Neighbors(FLANN)is used to complete the feature point matching method,and then the method based on improved minimum distance and random sampling consistency(RANSAC)are adopted to eliminate false matches,which applies PNP to solve the camera pose transformation relationship between adjacent frames.The backend optimization uses G2O(general graph optimization)to optimize the pose Graph,and uses loopback detection to eliminate the cumulative error.Experimental results prove that the proposed method is effective and feasible,which can quickly and accurately construct a threedimensional dense map.