Multi-channel Scan Context for LiDAR-based Place Recognition Using Siamese Neural Network
- Title
- Multi-channel Scan Context for LiDAR-based Place Recognition Using Siamese Neural Network
- Authors
- Park, chaewon; Yoon, Kwanwoong; Hong, Junwoo; Mun, Yeoungtae; HAN, SOOHEE
- Date Issued
- 2023-06-26
- Publisher
- KROS, IEEE
- Abstract
- LiDAR-based place recognition is a key component
of LiDAR-based localization. However, due to the unordered,
unstructured, and noisy characteristics of point cloud
data from LiDAR sensors, there are limitations in using raw
LiDAR data for place recognition. Therefore, it is essential to
perform conversion processing to generate LiDAR descriptors
for place recognition. In this paper, we propose a new method
for generating descriptors adopting the feature extraction
method of scan context.We attempt to improve the performance
of place recognition by implementing a multi-channel scan
context that combines geometric, semantic and intensity information.
Furthermore, utilizing the rotation-invariant Siamese
neural network, we propose a robust descriptor to handle
translation, roll and pitch motions. We verify the performance
of our descriptor generation and place recognition performance
through an experiment using the semantic KITTI dataset.
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/122516
- Article Type
- Conference
- Citation
- International Conference on Ubiquitous Robots (UR), page. 201 - 205, 2023-06-26
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- There are no files associated with this item.
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