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Laser-Induced Fluorescence Emission L.I.F.E. as Novel Non-Invasive Tool for In-Situ Measurements of Biomarkers in Cryospheric Habitats

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LFVB-BioSLAM: A Bionic SLAM System with a Light-Weight LiDAR Front End and a Bio-Inspired Visual Back End.

Ruilan Gao¹, Zeyu Wan¹, Sitong Guo¹

¹State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China.

Biomimetics (Basel, Switzerland)

|September 27, 2023

Summary

LFVB-BioSLAM is a novel bionic SLAM system combining LiDAR odometry and bio-inspired vision processing. This approach enhances accuracy and robustness in autonomous robot navigation compared to existing methods.

Keywords:

bionic robotics loop closure detection path integration simultaneous localization and mapping (SLAM)

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Area of Science:

Robotics
Artificial Intelligence
Computer Vision

Background:

Simultaneous Localization and Mapping (SLAM) is vital for autonomous robots but often power-intensive.
Bionic SLAM algorithms inspired by animal navigation are emerging but face accuracy and robustness challenges.
Existing bionic SLAM, like RatSLAM, struggles in complex environments.

Purpose of the Study:

To introduce LFVB-BioSLAM, a novel bionic SLAM system with improved practicality.
To develop a bionic SLAM framework facilitating research advancements.
To enhance autonomous robot navigation capabilities through bio-inspired design.

Main Methods:

Implemented a light-weight LiDAR-based front end using range flow for odometry estimation.
Developed a bio-inspired, vision-based back end utilizing monocular RGB cameras for loop closure and path integration.
Integrated LiDAR odometry with a biologically-inspired visual processing pipeline.

Main Results:

LFVB-BioSLAM demonstrated superior accuracy and robustness in real-world experiments.
The system outperformed RatSLAM (vision-based bionic SLAM) and RF2O (laser-based odometry).
The proposed framework offers a practical solution for bionic SLAM.

Conclusions:

LFVB-BioSLAM presents a significant advancement in bionic SLAM technology.
The hybrid LiDAR-vision approach enhances navigation system performance.
This work paves the way for more capable and efficient bio-inspired autonomous robots.