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Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

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Large-Scale LiDAR SLAM with Factor Graph Optimization on High-Level Geometric Features.

Krzysztof Ćwian¹, Michał R Nowicki¹, Jan Wietrzykowski¹

¹Institute of Robotics and Machine Intelligence, Poznan University of Technology, 60-965 Poznan, Poland.

Sensors (Basel, Switzerland)

|June 2, 2021

Summary

This summary is machine-generated.

This study introduces a LiDAR-based simultaneous localization and mapping (SLAM) system using high-level geometric features like line segments and planar patches. This approach enhances large-scale urban environment mapping and trajectory optimization.

Keywords:

3-D LiDAR SLAM bundle adjustment geometric features optimization

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Area of Science:

Robotics
Computer Vision
Geospatial Analysis

Background:

Visual SLAM methods excel with feature matching and optimization.
LiDAR SLAM commonly uses iterative closest point algorithms with raw point clouds.
High-level geometric features (lines, planes) offer richer environmental representation.

Purpose of the Study:

Analyze advantages of LiDAR SLAM using high-level geometric features in urban settings.
Develop a novel LiDAR SLAM approach leveraging planar patches and line segments.
Improve map representation and trajectory optimization for large-scale environments.

Main Methods:

Utilized planar patches and line segments for LiDAR SLAM map representation.
Employed factor graph optimization, common in visual SLAM, for final map and trajectory refinement.
Integrated learned descriptors for efficient loop closure and place recognition.
Built upon the LOAM (Lidar Odometry and Mapping) concept for real-time performance.

Main Results:

Demonstrated gains in large-scale LiDAR SLAM using high-level features and full optimization.
Achieved efficient loop closure through feature matching and factor graph optimization.
Maintained real-time operation based on the LOAM framework.
Experimental comparisons showed improvements in trajectory accuracy and map consistency over open-source LOAM.

Conclusions:

High-level geometric features significantly benefit large-scale LiDAR SLAM in urban environments.
The proposed factor graph optimization approach enhances trajectory accuracy and map consistency.
The system offers an efficient and robust solution for real-time LiDAR SLAM.