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Non-Repetitive Scanning LiDAR Sensor for Robust 3D Point Cloud Registration in Localization and Mapping Applications.

Ahmad K Aijazi1, Paul Checchin1

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Summary
This summary is machine-generated.

This study validates non-repetitive scanning LiDAR for 3D point cloud registration in autonomous navigation. A novel method using Spirograph-type scanning enhances localization and mapping efficiency.

Keywords:
3D point cloud and scan registrationLiDARSpirograph scanning patternnon-repetitive scan

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Area of Science:

  • Robotics and Autonomous Systems
  • Computer Vision
  • Geospatial Data Processing

Background:

  • 3D point cloud registration is crucial for autonomous navigation but faces challenges.
  • Non-repetitive scanning LiDAR sensors offer new possibilities for 3D data acquisition.
  • The feasibility of Spirograph-type LiDAR for robust registration requires investigation.

Purpose of the Study:

  • To explore the feasibility of Spirograph-type non-repetitive scanning LiDAR for robust 3D point cloud registration.
  • To propose and evaluate a novel registration method tailored to this unique sensor's scanning pattern.
  • To assess the sensor's suitability for localization and mapping applications.

Main Methods:

  • Characterization of data from a Spirograph-type non-repetitive scanning LiDAR sensor.
  • Development of a registration method utilizing the sensor's unique scanning pattern to reconstruct and compare scans.
  • Extraction of scene objects by comparing real and reconstructed scans to determine transformations.
  • Performance evaluation on real datasets against state-of-the-art methods, with enhancements using loop closure and a Curve Fitting Derivative Filter (CFDT).

Main Results:

  • The proposed method successfully registers successive 3D scans using the Spirograph-type scanning pattern.
  • Performance improvements were achieved through loop closure constraints and CFDT for trajectory estimation.
  • The sensor is demonstrated to be suitable for robust 3D point cloud registration.
  • The method shows comparable accuracy to existing techniques but significantly faster processing times.

Conclusions:

  • Spirograph-type non-repetitive scanning LiDAR is feasible for robust 3D point cloud registration.
  • The proposed registration method offers an efficient solution for autonomous navigation.
  • This sensor technology and registration approach advance the field of real-time localization and mapping.