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Place Recognition through Multiple LiDAR Scans Based on the Hidden Markov Model.

Linqiu Gui¹, Chunnian Zeng², Jie Luo²

¹School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China.

Sensors (Basel, Switzerland)

|June 19, 2024

Summary

This study introduces a multi-frame descriptor-matching approach using a hidden Markov model (HMM) for enhanced LiDAR localization in autonomous driving. The method improves place recognition accuracy and robustness for unmanned ground vehicles (UGV).

Keywords:

global descriptor hidden Markov model place recognition

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Area of Science:

Robotics
Computer Vision
Artificial Intelligence

Background:

Autonomous driving systems for unmanned ground vehicles (UGV) rely heavily on LiDAR localization with prior maps for safe operation.
Accurate initial pose estimation is crucial for system startup and recovery from tracking loss.
Current LiDAR-based place recognition methods struggle with accuracy due to reliance on single LiDAR keyframes.

Purpose of the Study:

To enhance the accuracy and robustness of LiDAR-based place recognition for UGVs in enclosed environments.
To address the limitations of single-frame descriptor matching in existing LiDAR localization systems.
To leverage multi-frame information for more reliable pose estimation.

Main Methods:

A novel multi-frame descriptor-matching approach is proposed, utilizing a hidden Markov model (HMM).
The method integrates information from multiple LiDAR frames to improve place recognition.
Performance is evaluated using the KITTI dataset.

Main Results:

The proposed multi-frame approach significantly improves place recognition performance compared to single-frame methods.
An average performance improvement of 5.8% was observed.
A maximum performance improvement of 15.3% was achieved.

Conclusions:

The hidden Markov model-based multi-frame descriptor matching enhances LiDAR localization accuracy and robustness for UGVs.
This method offers a significant advancement over single-frame descriptor matching techniques.
The findings contribute to safer and more reliable autonomous navigation in structured environments.