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A Review of Dynamic Object Filtering in SLAM Based on 3D LiDAR.

Hongrui Peng1, Ziyu Zhao1, Liguan Wang1,2

  • 1School of Resources and Safety Engineering, Central South University, Changsha 410083, China.

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Summary

This study reviews Simultaneous Localization and Mapping (SLAM) using 3D LiDAR in dynamic environments. It details methods for filtering dynamic objects to improve SLAM accuracy and robustness for applications like autonomous driving.

Keywords:
LiDARSLAMdynamic point cloud filtering

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Area of Science:

  • Robotics and Artificial Intelligence
  • Computer Vision
  • Sensor Fusion

Background:

  • Simultaneous Localization and Mapping (SLAM) using 3D LiDAR is crucial for autonomous systems.
  • Dynamic objects in real-world environments significantly degrade SLAM accuracy and robustness.
  • Existing research on SLAM in dynamic environments lacks comprehensive review.

Purpose of the Study:

  • To provide a comprehensive review of 3D LiDAR-based SLAM in dynamic environments.
  • To analyze the necessity and importance of dynamic object filtering in SLAM.
  • To discuss current methods and future trends in this research area.

Main Methods:

  • Categorization of dynamic object filtering methods in 3D point clouds (e.g., ray-tracing, visibility-based, segmentation-based).
  • Classification of dynamic objects and corresponding processing strategies within SLAM frameworks (online, post-processing, long-term).
  • Analysis of the development process and current state of dynamic SLAM.

Main Results:

  • Detailed introduction to mainstream dynamic object filtering techniques for 3D point clouds.
  • Summary of processing strategies for different dynamic object categories in SLAM.
  • Identification of research gaps and challenges in dynamic SLAM.

Conclusions:

  • Dynamic object filtering is essential for robust and accurate 3D LiDAR SLAM.
  • A structured approach to filtering methods and object classification is needed.
  • Future research should focus on advanced filtering techniques and long-term dynamic SLAM solutions.