【摘要】 面向服务机器人使用廉价的RGB-D摄像头自主构建室内3维地图的需求,提出一种鲁棒的图像对齐方法.基于特征点的匹配集计算帧间变换,使用随机抽样一致算法(RANSAC)消除误配,并改变其内点计数策略以适应特征点空间分布不均;同时检测地面信息,利用共面约束来增强点集对齐.在机器人从真实室内环境中采集的RGB-D图像序列上进行了实验,帧间对齐错误率为0,全局地面误差不超过2 cm;3维建图过程准确且能够连续进行.结果表明使用地面信息能有效提高地图的全局精度,方法兼备鲁棒性和准确性.更多还原

【Abstract】 A robust RGB-D image alignment method is proposed for autonomously building 3D maps for a service robot equipped with inexpensive RGB-D cameras. The transformation between frames is calculated based on matched sets of feature points, and the RANdom SAmpling Consensus(RANSAC) algorithm is used to eliminate false matchings, and the algorithm’s inlier counting policy is modified to adapt to spatial nonuniformity of feature points. Meanwhile, floor information is detected and the coplanar constraint is used to enhance alignment of point sets. Experiments are conducted on RGB-D image sequence collected by robot in a real indoor environment. The error rate of frame-to-frame alignment is zero, and the global floor error is less than 2 cm. The 3D mapping process can be implemented accurately and continuously. Results show that the floor information can effectively improve the global precision of the map, and the method is robust and accurate.更多还原