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Related Experiment Video

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Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

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Research on Simultaneous localization and mapping Algorithm based on Lidar and IMU.

Minghe Liu¹, Ye Tao¹, Zhongbo Wang¹

¹School of Computer Science and Software Engineering, University of Science and Technology Liaoning, China.

Mathematical Biosciences and Engineering : MBE

|May 10, 2023

Summary

This study enhances robot navigation by tightly coupling Lidar and Inertial Measurement Unit (IMU) data for improved Simultaneous Localization and Mapping (SLAM). Optimized factor graph methods with sliding windows boost mapping accuracy and speed for autonomous systems.

Keywords:

factor graph loopback detection multi-line lidar simultaneous positioning and mapping

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Area of Science:

Robotics
Computer Vision
Sensor Fusion

Background:

Autonomous driving and mobile robots rely on Simultaneous Localization and Mapping (SLAM).
Lidar and Inertial Measurement Unit (IMU) integration enhances SLAM accuracy.
Accurate environment modeling and real-time positioning are crucial for unmanned systems.

Purpose of the Study:

To propose an optimized SLAM method integrating multi-line Lidar and IMU data.
To improve mapping accuracy and real-time performance for mobile robots.
To enhance the robustness of autonomous systems in complex environments.

Main Methods:

A tightly coupled factor graph optimization approach for Lidar-IMU fusion.
Sliding window optimization to manage computational load and maintain accuracy.
Integration of scanning context loopback detection for enhanced mapping.
Point plane matching for environment modeling.

Main Results:

The factor graph optimization method significantly improved mapping quality and reduced errors.
Sliding window optimization enhanced processing speed without compromising accuracy.
Loopback detection further refined mapping accuracy and reduced frame matching time.
The proposed methods meet real-time positioning and mapping requirements.

Conclusions:

Tightly coupling Lidar and IMU data with factor graph optimization is effective for SLAM.
Sliding window and loopback detection techniques enhance both the speed and accuracy of the SLAM system.
The developed approach provides a robust foundation for real-time autonomous navigation.