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LeGO-LOAM-SC: An Improved Simultaneous Localization and Mapping Method Fusing LeGO-LOAM and Scan Context for

Guanghui Xue1,2, Jinbo Wei1, Ruixue Li1

  • 1School of Mechanical Electronic and Information Engineering, China University of Mining and Technology, Beijing 100083, China.

Sensors (Basel, Switzerland)
|January 22, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces LeGO-LOAM-SC, an improved Simultaneous Localization and Mapping (SLAM) algorithm for autonomous vehicles. It enhances mapping accuracy and real-time performance in complex environments like underground mines.

Keywords:
ICP graph optimizationLeGO-LOAMloop detectionscan contextsimultaneous localization and mappingunmanned vehicle

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Area of Science:

  • Robotics
  • Computer Vision
  • Autonomous Systems

Background:

  • Simultaneous Localization and Mapping (SLAM) is crucial for autonomous vehicle navigation in complex environments.
  • Existing SLAM algorithms face challenges in mapping accuracy and real-time performance, particularly for underground operations.
  • Improvements are needed for reliable unmanned and autonomous operation in challenging terrains.

Purpose of the Study:

  • To develop an enhanced SLAM algorithm for improved mapping accuracy and real-time performance in underground environments.
  • To integrate scan context with LeGO-LOAM for robust loop detection and pose estimation.
  • To reduce drift and enhance the reliability of autonomous vehicle navigation.

Main Methods:

  • Integration of Scan Context global descriptor for loop detection.
  • Incorporation of pose constraints via Iterative Closest Points (ICP) into Georgia Tech Smoothing and Mapping (GTSAM) for graph optimization.
  • Development of the LeGO-LOAM-SC algorithm combining Scan Context and LeGO-LOAM.

Main Results:

  • Significant reduction in Absolute Trajectory Error (ATE) by 55.7% and Relative Pose Error (RPE) by 50.3%.
  • Improved translation and rotation accuracy by approximately 5% with 2-4% reduction in time consumption.
  • Demonstrated higher mapping accuracy, better real-time performance, and reduced CPU occupancy (6% reduction).

Conclusions:

  • LeGO-LOAM-SC effectively addresses point cloud map drift, enhancing accuracy and real-time capabilities.
  • The algorithm provides more accurate trajectory estimation and smoother loop closures for autonomous vehicles.
  • This improved SLAM approach is suitable for accurate map construction and low-drift pose estimation in challenging underground settings.