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Related Experiment Video

Updated: Feb 28, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

VCC: Vertical Feature and Circle Combined Descriptor for 3D Place Recognition.

Wenguang Li^1,2, Yongxin Ma^1,2, Jiying Ren^1,2

¹School of Mechanical Engineering, Shandong University, Jinan 250061, China.

Sensors (Basel, Switzerland)

|February 27, 2026

Summary

This study introduces the Vertical Feature and Circle Combined (VCC) descriptor for LiDAR-based SLAM. VCC enhances loop closure detection by providing efficient, rotation-invariant place recognition.

Keywords:

VCC descriptor place recognition vertical feature voxelization

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Last Updated: Feb 28, 2026

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Area of Science:

Robotics
Computer Vision
Simultaneous Localization and Mapping (SLAM)

Background:

Loop closure detection is crucial for LiDAR-based SLAM, but faces challenges with environmental variations.
Existing descriptors struggle with efficiency and robustness in place recognition.

Purpose of the Study:

To propose a novel composite descriptor, the Vertical Feature and Circle Combined (VCC) descriptor.
To enhance the efficiency and robustness of loop closure detection in LiDAR-based SLAM.

Main Methods:

Developed the VCC descriptor, a 3D local descriptor utilizing vertical features and circular histograms.
Voxelized point clouds to extract vertical features and encoded them into circular arc-based histograms.
Ensured rotation-invariant properties and robustness to viewpoint changes.

Main Results:

The VCC descriptor significantly improved feature representation efficiency and loop closure recognition performance.
Achieved loop closure retrieval within 30 ms, meeting real-time operation requirements.
Demonstrated superior performance compared to other descriptors on various datasets.

Conclusions:

The VCC descriptor offers a compact, efficient, and rotation-invariant environmental representation.
VCC is highly suitable for enhancing LiDAR-based SLAM systems.
The proposed method addresses critical challenges in robust place recognition for autonomous systems.