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Review

A Review of 2D Lidar SLAM Research

College of Mechanical and Electrical Engineering, Hohai University, Changzhou 213200, China
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Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(7), 1214; https://doi.org/10.3390/rs17071214
Submission received: 17 December 2024 / Revised: 11 March 2025 / Accepted: 20 March 2025 / Published: 28 March 2025

Abstract

Two-dimensional (2D) simultaneous localization and mapping (SLAM) is a key technology for intelligent indoor robots. By using a map generated via SLAM, the robot can navigate and perform specific tasks. This paper reviews the progress of 2D Lidar SLAM algorithms based on four principles: filter-based SLAM, matching-based SLAM, graph optimization-based SLAM, and deep learning-based SLAM, highlighting their advantages, disadvantages, and applicability. Additionally, two key research topics in 2D Lidar SLAM are presented: solutions for dynamic objects during mapping and the fusion of 2D Lidar and vision data. Finally, the development trends of 2D SLAM are discussed.
Keywords: lidar SLAM; graph optimization; deep learning; dynamic objects; changing environment lidar SLAM; graph optimization; deep learning; dynamic objects; changing environment

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MDPI and ACS Style

Ran, Y.; Xu, X.; Tan, Z.; Luo, M. A Review of 2D Lidar SLAM Research. Remote Sens. 2025, 17, 1214. https://doi.org/10.3390/rs17071214

AMA Style

Ran Y, Xu X, Tan Z, Luo M. A Review of 2D Lidar SLAM Research. Remote Sensing. 2025; 17(7):1214. https://doi.org/10.3390/rs17071214

Chicago/Turabian Style

Ran, Yingying, Xiaobin Xu, Zhiying Tan, and Minzhou Luo. 2025. "A Review of 2D Lidar SLAM Research" Remote Sensing 17, no. 7: 1214. https://doi.org/10.3390/rs17071214

APA Style

Ran, Y., Xu, X., Tan, Z., & Luo, M. (2025). A Review of 2D Lidar SLAM Research. Remote Sensing, 17(7), 1214. https://doi.org/10.3390/rs17071214

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