Online Extrinsic Calibration on LiDAR-Camera System with LiDAR Intensity Attention and Structural Consistency Loss Article Swipe
Pei An
,
Yingshuo Gao
,
Liheng Wang
,
Yanfei Chen
,
Jie Ma
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/rs14112525
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/rs14112525
Extrinsic calibration on a LiDAR-camera system is an essential task for the advanced perception application for the intelligent vehicle. In the offline situation, a calibration object based method can estimate the extrinsic parameters in high precision. However, during the long time application of LiDAR-camera system in the actual scenario, the relative pose of LiDAR and camera has small and accumulated drift, so that the offline calibration result is not accurate. To correct the extrinsic parameter conveniently, we present a deep learning based online extrinsic calibration method in this paper. From Lambertian reflection model, it is found that an object with higher LiDAR intensity has the higher possibility to have salient RGB features. Based on this fact, we present a LiDAR intensity attention based backbone network (LIA-Net) to extract the significant co-observed calibration features from LiDAR data and RGB image. In the later stage of training, the loss of extrinsic parameters changes slowly, causing the risk of vanishing gradient and limiting the training efficiency. To deal with this issue, we present the structural consistency (SC) loss to minimize the difference between projected LiDAR image (i.e., LiDAR depth image, LiDAR intensity image) and its ground truth (GT) LiDAR image. It aims to accurately align the LiDAR point and RGB pixel. With LIA-Net and SC loss, we present the convolution neural network (CNN) based calibration network LIA-SC-Net. Comparison experiments on a KITTI dataset demonstrate that LIA-SC-Net has achieved more accurate calibration results than state-of-the-art learning based methods. The proposed method has both accurate and real-time performance. Ablation studies also show the effectiveness of proposed modules.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs14112525
- https://www.mdpi.com/2072-4292/14/11/2525/pdf?version=1653469244
- OA Status
- gold
- Cited By
- 11
- References
- 67
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4281489542
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4281489542Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/rs14112525Digital Object Identifier
- Title
-
Online Extrinsic Calibration on LiDAR-Camera System with LiDAR Intensity Attention and Structural Consistency LossWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-05-25Full publication date if available
- Authors
-
Pei An, Yingshuo Gao, Liheng Wang, Yanfei Chen, Jie MaList of authors in order
- Landing page
-
https://doi.org/10.3390/rs14112525Publisher landing page
- PDF URL
-
https://www.mdpi.com/2072-4292/14/11/2525/pdf?version=1653469244Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2072-4292/14/11/2525/pdf?version=1653469244Direct OA link when available
- Concepts
-
Lidar, Calibration, Computer science, Artificial intelligence, Remote sensing, Computer vision, RGB color model, Geology, Mathematics, StatisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
11Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3, 2024: 6, 2023: 1, 2022: 1Per-year citation counts (last 5 years)
- References (count)
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67Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| primary_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
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| publication_date | 2022-05-25 |
| publication_year | 2022 |
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