ScatterHough: Automatic Lane Detection from Noisy LiDAR Data Article Swipe
Honghao Zeng
,
Jiang Shihong
,
Tianxiang Cui
,
Zheng Lu
,
Jiawei Li
,
Boon Giin Lee
,
Junsong Zhu
,
Xiaoying Yang
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/s22145424
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/s22145424
Lane detection plays an essential role in autonomous driving. Using LiDAR data instead of RGB images makes lane detection a simple straight line, and curve fitting problem works for realtime applications even under poor weather or lighting conditions. Handling scatter distributed noisy data is a crucial step to reduce lane detection error from LiDAR data. Classic Hough Transform (HT) only allows points in a straight line to vote on the corresponding parameters, which is not suitable for data in scatter form. In this paper, a Scatter Hough algorithm is proposed for better lane detection on scatter data. Two additional operations, ρ neighbor voting and ρ neighbor vote-reduction, are introduced to HT to make points in the same curve vote and consider their neighbors’ voting result as well. The evaluation of the proposed method shows that this method can adaptively fit both straight lines and curves with high accuracy, compared with benchmark and state-of-the-art methods.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s22145424
- https://www.mdpi.com/1424-8220/22/14/5424/pdf?version=1658372622
- OA Status
- gold
- Cited By
- 12
- References
- 28
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4286208817
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4286208817Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/s22145424Digital Object Identifier
- Title
-
ScatterHough: Automatic Lane Detection from Noisy LiDAR DataWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
-
2022-07-20Full publication date if available
- Authors
-
Honghao Zeng, Jiang Shihong, Tianxiang Cui, Zheng Lu, Jiawei Li, Boon Giin Lee, Junsong Zhu, Xiaoying YangList of authors in order
- Landing page
-
https://doi.org/10.3390/s22145424Publisher landing page
- PDF URL
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https://www.mdpi.com/1424-8220/22/14/5424/pdf?version=1658372622Direct 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
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https://www.mdpi.com/1424-8220/22/14/5424/pdf?version=1658372622Direct OA link when available
- Concepts
-
Hough transform, Computer science, Benchmark (surveying), Lidar, Artificial intelligence, Line (geometry), Computer vision, Pattern recognition (psychology), Image (mathematics), Remote sensing, Mathematics, Geography, Geodesy, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
12Total citation count in OpenAlex
- Citations by year (recent)
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2025: 6, 2024: 4, 2023: 2Per-year citation counts (last 5 years)
- References (count)
-
28Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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