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LiDAR localization using position-encoded landmarks without point cloud maps
Xie, Wenjing Ren, Tianchi Xue, Chun Jason Wu, Jen-Ming Guan, Nan
Xie, Wenjing
Ren, Tianchi
Xue, Chun Jason
Wu, Jen-Ming
Guan, Nan
Supervisor
Department
Computer Science
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Type
Journal article
Date
2025
License
Language
English
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Abstract
LiDAR-based localization plays a critical role in autonomous driving and robotic navigation. However, traditional methods rely heavily on constructing high-precision point cloud maps, which is both time-consuming and labor-intensive. To address this, we propose an innovative localization approach that eliminates the need for point cloud maps by leveraging LiDAR and position-encoded landmarks. Our method encodes positional information into the shape of specially designed landmarks, strategically deployed in the environment. Subsequently, we fully leverage the advantages of LiDAR in accurately measuring distances and capturing the spatial structures of objects to detect and recognize the landmarks in the environment. By decoding the positional information embedded in the landmarks, precise vehicle localization is achieved. To overcome the limited information capacity of individual landmarks due to LiDAR’s reduced accuracy at long distances, we integrate multiple landmarks in a collaborative manner. By combining their encoded information and spatial relationships, we achieve high-precision localization without relying on point cloud maps. Experiments in CARLA and Autoware.AI simulators validate the effectiveness of our approach, offering a novel solution for LiDAR-based localization.
Citation
W. Xie, T. Ren, C. J. Xue, J.-M. Wu, and N. Guan, “LiDAR localization using position-encoded landmarks without point cloud maps,” Journal of Systems Architecture, vol. 168, p. 103556, Nov. 2025, doi: 10.1016/J.SYSARC.2025.103556
Source
Journal of Systems Architecture
Conference
Keywords
LiDAR, Position-Encoded Landmarks, Vehicle Localization, Point Cloud Map-Free
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Source
Publisher
Elsevier