Evaluation of Range Sensing-based Place Recognition for Long-term Urban Localization

作     者：Ma, Weixin Yin, Huan Yao, Lei Sun, Yuxiang Su, Zhongqing 

作者机构：Department of Mechanical Engineering The Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Department of Electrical and Electronic Engineering The Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong Department of Mechanical Engineering City University of Hong Kong Kowloon Hong Kong 

出 版 物：《IEEE Transactions on Intelligent Vehicles》 (IEEE Trans. Intell. Veh.)

年 卷 期：2024年第9卷第5期

页      面：1-12页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 08[工学] 0803[工学-光学工程] 

基　　金：Hong Kong Research Grants Council Hong Kong Innovation and Technology Commission City University of Hong Kong 

主　　题：Optical radar 

摘      要：Place recognition is a critical capability for autonomous vehicles. It matches current sensor data with a pre-built database to provide coarse localization results. However, the effectiveness of long-term place recognition may be degraded by environment changes, such as seasonal or weather changes. To have a deep understanding of this issue, we conduct a comprehensive evaluation study on several state-of-the-art range sensing-based (i.e., LiDAR and radar) place recognition methods on the Borease dataset, which encapsulates long-term localization scenarios with stark seasonal variations and adverse weather conditions. In addition, we design a novel metric to evaluate the influence of matching thresholds on place recognition performance for long-term localization. Our results and findings provide fresh insights to the community and potential directions for future study. IEEE