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When-to-Loop: Enhanced Loop Closure for LiDAR SLAM in Urban Environments Based on SCAN CONTEXT.

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This study introduces a new loop closure detection method for LiDAR-based SLAM, improving navigation accuracy by using drivable areas and IMU data. The enhanced SCAN CONTEXT algorithm significantly reduces accumulated errors in urban environments.

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Area of Science:

  • Robotics
  • Computer Vision
  • Autonomous Systems

Background:

  • Global Navigation Satellite Systems (GNSSs) struggle with accuracy in urban canyons due to signal obstruction.
  • Micro-Electro-Mechanical System (MEMS) Inertial Measurement Units (IMUs) offer autonomous navigation but suffer from error accumulation.
  • LiDAR-based Simultaneous Localization and Mapping (SLAM) systems are used but face drift and error issues.

Purpose of the Study:

  • To develop a novel loop closure detection method for LiDAR-based SLAM.
  • To correct time-accumulated errors by identifying previously visited locations.
  • To enhance the robustness and accuracy of autonomous navigation systems.

Main Methods:

  • Leveraging vehicular drivable area and IMU trajectory for keyframe selection, identifying significant environmental changes.
  • Extending the SCAN CONTEXT algorithm to incorporate overall point cloud distribution for robust loop closure constraints.
  • Utilizing enhanced environmental feature extraction beyond simple height-based metrics.

Main Results:

  • Achieved a 6% overall accuracy improvement on the KITTI dataset.
  • Demonstrated a 17% accuracy improvement in local scenarios, showcasing enhanced robustness.
  • Validated the effectiveness of the proposed method in correcting accumulated errors.

Conclusions:

  • The novel loop closure detection method significantly improves LiDAR-based SLAM accuracy and robustness.
  • Integrating drivable area and IMU data offers a more reliable approach to keyframe selection.
  • The extended SCAN CONTEXT algorithm provides stronger loop closure constraints for autonomous navigation.