面向室外移动机器人的雷达–图像跨模态检索  

Radar-image cross-modal retrieval for outdoor mobile robots

作　　者：张凯 刘华平[2] 邓晓燕 马晓健 张新钰 ZHANG Kai;LIU Hua-ping;DENG Xiao-yan;MA Xiao-jian;ZHANG Xin-yu(College of Electrical and Electronic Engineering, Shijiazhuang Tiedao University, Shijiazhuang Hebei 050043, China;Department of Computer Science and Technology, Tsinghua University,State Key Laboratory of Intelligent Technology and Systems, Beijing 100084, China)

机构地区：[1]石家庄铁道大学电气与电子工程学院,河北石家庄050043 [2]清华大学计算机科学与技术系智能技术与系统国家重点实验室,北京100084

出　　处：《控制理论与应用》2018年第12期1759-1764,共6页Control Theory & Applications

基　　金：国家自然科学基金重点项目(U1613212);北京市科技计划项目(D171100005017002);河北省研究生创新资助项目(Z671180101)资助.

摘　　要：移动机器人主要依靠激光雷达采集的点云和摄像机采集的图像信息来感知周围环境.在极端天气或夜晚的情况下,摄像机采集图像会受到极大干扰;本文基于聚类典型相关分析(cluster-canonical correlation analysis,cluster–CCA)提出一种面向室外移动机器人的雷达图像跨模态检索技术,首先利用深度学习网络提取点云和图像的特征,然后使用聚类典型相关分析将两种模态的特征映射到子空间,最后计算欧氏距离进行检索,可以从图像数据库中检索得出与点云最相似的图像文件.本文所提出的方法在KITTI数据集上进行了验证,实现了从点云到图像的跨模态检索,结果验证了cluster–CCA在室外移动机器人雷达图像检索方面应用的有效性.Mobile robots mainly rely on point clouds and image information collected by laser radar and cameras to sense the surrounding environment. In extreme weather or at night, the camera captures images will be greatly disturbed;this paper proposes a radar image cross-modal retrieval technique for outdoor mobile robots based on cluster-canonical correlation analysis (cluster–CCA). Firstly, deep learning networks are used to extract the characteristics of the point cloud and the image, then using clustering canonical correlation analysis to map the features of the two modes to the subspace, and finally calculate the Euclidean distance to retrieve and get images, and the image files most similar to the point cloud can be obtained from the image database. The method proposed in this paper is tested on the KITTI data set, and achieves cross-modal retrieval from point cloud to image. The result validates the effectiveness of cluster–CCA in radar image retrieval for outdoor mobile robots.

关 键 词：移动机器人 点云 图像 特征提取 深度学习 聚类典型相关分析 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]