融合图特征的多机器人栅格地图拼接方法  

Multi-Robot Grid Map Stitching Method Combining Graph Features

作　　者：黄小杭 曾碧[1] 刘建圻[1] 汪明慧[1] HUANG Xiaohang;ZENG Bi;LIU Jianqi;WANG Minghui(School of Computers,Guangdong University of Technology,Guangzhou 510006,China)

机构地区：[1]广东工业大学计算机学院,广州510006

出　　处：《计算机工程与应用》2022年第14期299-305,共7页Computer Engineering and Applications

基　　金：广东省自然科学基金(2018A030313868,2018A030310540);中山市重大科技专项(191021082628279)。

摘　　要：现有的栅格地图拼接方法在地图重叠区域较小、地图特征较少、地图存在自相似性和非刚性形变的情况下匹配精度往往会大幅度下降甚至失配,提出了一种融合图特征的多机器人栅格地图拼接方法。提取待匹配栅格地图的ORB特征点并粗匹配,接下来建立ORB特征点之间的中值K近邻图;建立最优传输目标函数并融合ORB特征和图特征构建传输代价矩阵,同时建立增广节点筛选通过Sinkhorn算法求解最优匹配,RANSAC算法求解两张栅格地图之间的刚体变换,实现多机器人栅格地图的配准和拼接。通过实验验证了该方法具备较高的拼接精度,可应对重叠率低、特征不太明显的场景,展现出了较快的计算速度,并分析了相关参数对算法表现的影响。Existing gird map stitching methods tend to degrade the matching accuracy significantly when the map overlap area is small,the map features are few,and there are self-similarities and non-rigid deformations in the map.In this paper,a multi-robot gird map stitching method for combining graph features is presented.First,it extracts the ORBs of the gird map to be matched and coarsely matches them,then builds a median K-nearest-neighbor map between the ORBs.Then it builds an optimal transport objective function and fuses the ORBs and map features to build a transport cost matrix,and builds a broadening node filter to solve the optimal match by Sinkhorn algorithm and the rigid body transformation between the two gird maps by RANSAC.In this paper,it experimentally verifies that the method has high stitching accuracy and can cope with low overlap rate and less obvious features,shows fast computation speed,and analyzes the influence of rele-vant parameters on the performance of the method.

关 键 词：图匹配 最优传输 多机器人 图像拼接 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]