Cylindrical Scan Context: A Multi-Channel Descriptor for Vertical-Structure-Aware LiDAR Localization
This study introduces Cylindrical Scan Context (CSC), a novel LiDAR descriptor designed to improve robustness and efficiency in GPS-denied or degraded outdoor environments. Unlike the conventional Scan Context (SC), which relies on azimuth-range projection, CSC employs an azimuth-height representation that preserves vertical structural information and incorporates multiple physical channels-range, point density, and reflectance intensity-to capture both geometric and radiometric characteristics of the environment. This multi-channel cylindrical formulation enhances descriptor distinctiveness and robustness against viewpoint, elevation, and trajectory variations. To validate the effectiveness of CSC, real-world experiments were conducted using both self-collected coastal-forest datasets and the public MulRan-KAIST dataset. Mapping was performed using LIO-SAM with LiDAR, IMU, and GPS measurements, after which LiDAR-only localization was evaluated independently. A total of approximately 700 query scenes (1 m ground-truth threshold) were used in the self-collected experiments, and about 1200 scenes (3 m threshold) were evaluated in the MulRan-KAIST experiments. Comparative analyses between SC and CSC were performed using Precision-Recall (PR) curves, Detection Recall (DR) curves, Root Mean Square Error (RMSE), and Top-<i>K</i> retrieval accuracy. The results show that CSC consistently yields lower RMSE-particularly in the vertical and lateral directions-and demonstrates faster recall growth and higher stability in global retrieval. Across datasets, CSC maintains superior DR performance in high-confidence regions and achieves up to 45% reduction in distance RMSE in large-scale campus environments. These findings confirm that the cylindrical multi-channel formulation of CSC significantly improves geometric consistency and localization reliability, offering a practical and robust LiDAR-only localization framework for challenging unstructured outdoor environments.
Authors
- Jongho Bae (ORCID: https://orcid.org/0000-0002-2315-6310)
- Gun Rae Cho (ORCID: https://orcid.org/0000-0002-8536-4844)
- Jung-Hyeun Park (ORCID: https://orcid.org/0000-0002-0347-9219)
- Chulhee Bae (ORCID: https://orcid.org/0000-0002-6030-8803)
- S.J. Kim (ORCID: https://orcid.org/0009-0000-7307-5749)
- M. J. Lee (ORCID: https://orcid.org/0000-0003-4961-9616)
- Sungho Park (ORCID: https://orcid.org/0000-0002-2208-9128)
Institutions
- Korea Institute of Robot and Convergence (KR)
Publication Details
- Journal
- Sensors
- Published
- 2025-11-26
- DOI
- https://doi.org/10.3390/s25237223
- Primary Topic
- Remote Sensing and LiDAR Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00