Radar style transfer for metric robot localisation on lidar maps  

作　　者：Huan Yin Yue Wang Jun Wu Rong Xiong 

机构地区：[1]College of Control Science and Engineering,Zhejiang University,Hangzhou,China [2]Department of Electronic and Computer Engineering,Hong Kong University of Science and Technology,Kowloon,Hong Kong,China

出　　处：《CAAI Transactions on Intelligence Technology》2023年第1期139-148,共10页智能技术学报（英文）

基　　金：National Key R&D Program of China,Grant/Award Number:2020YFB1313300;National Nature Science Foundation of China under Grant,Grant/Award Number:61903332;Hong Kong Center for Construction Robotics(InnoHK center supported by Hong Kong ITC)。

摘　　要：Lidar and visual data are affected heavily in adverse weather conditions due to sensing mechanisms,which bring potential safety hazards for vehicle navigation.Radar sensing is desirable to build a more robust navigation system.In this paper,a cross-modality radar localisation on prior lidar maps is presented.Specifically,the proposed workflow consists of two parts:first,bird's-eye-view radar images are transferred to fake lidar images by training a generative adversarial network offline.Then with online radar scans,a Monte Carlo localisation framework is built to track the robot pose on lidar maps.The whole online localisation system only needs a rotating radar sensor and a pre-built global lidar map.In the experimental section,the authors conduct an ablation study on image settings and test the proposed system on Oxford Radar Robot Car Dataset.The promising results show that the proposed localisation system could track the robot pose successfully,thus demonstrating the feasibility of radar style transfer for metric robot localisation on lidar maps.

关 键 词：LIDAR local MAPS 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]