A Lightweight High Definition Mapping Method Based on Multi-Source Data Fusion Perception

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 gene...

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Bibliographic Details
Published inApplied sciences Vol. 13; no. 5; p. 3264
Main Authors Song, Haina, Hu, Binjie, Huang, Qinyan, Zhang, Yi, Song, Jiwei
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.03.2023
Subjects
Accuracy
Algorithms
Analysis
Calibration
Cameras
Clustering
crowdsourced data
Crowdsourcing
Data integration
Global positioning systems
GPS
High definition
high-definition map
Lightweight
Mapping
Methods
multi-source data fusion perception
Multisensor fusion
Perception
semantic aggregation
semantic alignment
Semantics
Sensors
China
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Summary: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.
ISSN:2076-3417
2076-3417
DOI:10.3390/app13053264