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A Novel Loop Closure Detection Approach Using Simplified Structure for Low-Cost LiDAR.

Qin Ye¹, Pengcheng Shi¹, Kunyuan Xu²

¹College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China.

Sensors (Basel, Switzerland)

|April 23, 2020

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This study introduces a new method for loop closure detection (LCD) in simultaneous localization and mapping (SLAM) using a low-cost lidar. The approach enhances robot navigation stability and mapping accuracy by efficiently identifying loops in point cloud data.

Area of Science:

Robotics
Computer Vision
Artificial Intelligence

Background:

Simultaneous Localization and Mapping (SLAM) requires reducing cumulative error for stable robot navigation.
Loop Closure Detection (LCD) is vital for correcting accumulated errors in SLAM.
Existing point cloud-based LCD methods face challenges with sensor resolution, efficiency, and accuracy.

Purpose of the Study:

To propose a novel, fast, and accurate global LCD method for SLAM using a low-cost 16-beam lidar.
To address the limitations of current LCD techniques in terms of sensor dependency and performance.
To improve global localization stability and reduce mapping errors in indoor environments.

Main Methods:

Extraction and hierarchical management of "Simplified Structure" from indoor point clouds.

Keywords:

hierarchical structure matching indoor scene loop closure detection point cloud registration suitability simplified structure

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A pre-match analysis and hierarchical matching strategy with geometric constraints for scan registration.

A multi-state loop evaluation model for multi-level structures to confirm loop closures.

Main Results:

The proposed method achieves high efficiency and accuracy in global loop closure detection.
Experimental results show average global LCD precision of 0.90, accuracy of 0.96, and negative rate of 0.97.
The method successfully provides point cloud registration transformations upon successful loop detection.

Conclusions:

The novel "Simplified Structure" based LCD method is effective for low-cost lidar systems.
This approach significantly enhances the performance of SLAM and robot navigation in indoor settings.
The method offers a practical solution for accurate and efficient loop closure detection.