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VCC: Vertical Feature and Circle Combined Descriptor for 3D Place Recognition
Wenguang Li1,2, Yongxin Ma1,2, Jiying Ren1,2
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.
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.
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