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Continuous-Time Laser Frames Associating and Mapping via Multilayer Optimization.

Shaoxing Hu¹, Shen Xiao¹, Aiwu Zhang^2,3

¹School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China.

Sensors (Basel, Switzerland)

|December 30, 2020

Summary

This summary is machine-generated.

This study presents a multilayer optimization approach for simultaneous localization and mapping (SLAM) using continuous-time laser frames. The method achieves accurate 3D point cloud mapping and low-drift localization without external navigation aids.

Keywords:

SLAM interframe association loop detection multilayer optimization submap matching

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Area of Science:

Robotics
Computer Vision
Geomatics

Background:

Simultaneous Localization and Mapping (SLAM) is crucial for mobile robots and autonomous systems.
Associating continuous-time laser frames presents significant challenges for hand-held or backpack SLAM systems.
Existing methods often rely on Global Navigation Satellite System (GNSS) or Inertial Navigation System (INS) for localization.

Purpose of the Study:

To develop a robust method for associating continuous-time laser frames for accurate SLAM.
To model the SLAM problem as a multilayer optimization for low-drift localization and point cloud reconstruction.
To enable accurate mapping without the assistance of GNSS/INS navigation systems.

Main Methods:

A multilayer optimization framework is proposed for SLAM.
Normal Distributions Transform (NDT) and Iterative Closest Point (ICP) algorithms are used for 3D point cloud alignment.
Automatic ground point extraction, non-ground point segmentation, and noise point omission are performed.
Three levels of association are employed: inter-frame, submap matching, and closed-loop optimization.

Main Results:

The proposed method accurately associates continuous-time laser frames, ensuring 3D point cloud map consistency.
Experimental results demonstrate accurate mapping capabilities even in complex environments.
The method effectively handles sparse laser frames, crucial for sensors like the Velodyne VLP-16.

Conclusions:

The multilayer optimization approach provides a robust solution for continuous-time laser frame association in SLAM.
The method achieves high accuracy in localization and mapping without relying on external navigation systems.
This approach enhances the performance of SLAM systems, particularly those using sparse or low-frequency laser data.