A Lightweight High Definition Mapping Method Based on Multi-Source Data Fusion Perception
-
Published:2023-03-03
Issue:5
Volume:13
Page:3264
-
ISSN:2076-3417
-
Container-title:Applied Sciences
-
language:en
-
Short-container-title:Applied Sciences
Author:
Song Haina1ORCID, Hu Binjie1, Huang Qinyan2, Zhang Yi3, Song Jiwei4
Affiliation:
1. Department of School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China 2. Guangzhou Jiaoxintou Technology Co., Ltd., Guangzhou 510100, China 3. Guangzhou Laneposition Technology Co., Ltd., Guangzhou 511455, China 4. China Electronics Standardization Institute, Beijing 100007, China
Abstract
In this paper, a lightweight, high-definition mapping method is proposed for autonomous driving to address the drawbacks of traditional mapping methods, such as high cost, low efficiency, and slow update frequency. The proposed method is based on multi-source data fusion perception and involves generating local semantic maps (LSMs) using multi-sensor fusion on a vehicle and uploading multiple LSMs of the same road section, obtained through crowdsourcing, to a cloud server. An improved, two-stage semantic alignment algorithm, based on the semantic generalized iterative closest point (GICP), was then used to optimize the multi-trajectories pose on the cloud. Finally, an improved density clustering algorithm was proposed to instantiate the aligned semantic elements and generate vector semantic maps to improve mapping efficiency. Experimental results demonstrated the accuracy of the proposed method, with a horizontal error within 20 cm, a vertical error within 50 cm, and an average map size of 40 Kb/Km. The proposed method meets the requirements of being high definition, low cost, lightweight, robust, and up-to-date for autonomous driving.
Funder
Ministry of Industry and Information Technology
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference39 articles.
1. An introduction to the technology: Mobile mapping systems;Petrie;Geoinformatics,2020 2. High Definition Map for Automated Driving: Overview and Analysis;Liu;J. Naving.,2020 3. Ulbrich, S., Reschka, A., Rieken, J., Ernst, S., Bagschik, G., Dierkes, F., Nolte, M., and Maurer, M. (2017). Towards a functional system architecture for automated vehicles. arXiv. 4. Review of advanced driver assistance systems (ADAS);Cupek;AIP Conf. Proc.,2017 5. Fully Autonomous Buses: A Literature Review and Future Research Directions;Azad;J. Adv. Transp.,2019
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|