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2D LiDAR SLAM Back-End Optimization with Control Network Constraint for Mobile Mapping.

Jingren Wen¹, Chuang Qian², Jian Tang³

¹GNSS Research Centre, Wuhan University, 129 Luoyu Road, Wuhan 430079, China. jrwen@whu.edu.cn.

Sensors (Basel, Switzerland)

|November 2, 2018

Summary

This summary is machine-generated.

This study introduces a control network constraint (CNC) method for graph-based Simultaneous Localization and Mapping (SLAM) using Light Detection and Ranging (LiDAR). This approach improves mapping accuracy by optimizing poses without relying on loop closure, achieving a 0.3614 m RMS error in GNSS-weak areas.

Keywords:

Delaunay triangulation network SLAM back-end optimization distance constraint mobile mapping

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Area of Science:

Robotics and Autonomous Systems
Geomatics Engineering
Computer Vision

Background:

Simultaneous Localization and Mapping (SLAM) is crucial for robotic positioning and mapping.
Graph-based SLAM offers improved accuracy and efficiency over older methods like EKF and particle filters.
Loop closure is vital for error correction in SLAM but challenging for 2D LiDAR due to limited features and certain environments.

Purpose of the Study:

To propose and evaluate a novel method for enhancing graph-based 2D LiDAR SLAM.
To introduce Control Network Constraint (CNC) as an alternative to loop closure for optimizing robot poses and map accuracy.
To address the limitations of feature extraction in 2D LiDAR scans and environments lacking loop closure opportunities.

Main Methods:

Implemented a graph-based SLAM framework incorporating Control Network Constraint (CNC).
Aligned LiDAR scan centers with control vertices from a pre-surveyed control network for pose optimization.
Optimized scan and submap poses using CNC during the back-end processing stage.

Main Results:

Achieved a position Root Mean Square (RMS) error of 0.3614 m for key points, validated against a Terrestrial Laser Scanner (TLS) map.
Demonstrated significant improvement in mapping accuracy compared to SLAM without CNC (RMS error of 1.6462 m).
Validated the method's effectiveness in a typical urban Global Navigation Satellite System (GNSS) weak outdoor environment.

Conclusions:

Introducing Control Network Constraint (CNC) to graph-based SLAM is an effective and practical solution for mitigating drift accumulation in 2D LiDAR systems.
CNC provides a robust alternative to loop closure, particularly in scenarios with limited distinct features or environments where loop closure is not feasible.
The proposed method significantly enhances the accuracy and reliability of LiDAR-based mapping in challenging outdoor conditions.