Automated Pipeline for Multi-LiDAR Extrinsic Calibration: Experimental Evaluation and Performance Analysis Article Swipe
Surbhi Barnwal
,
Mansi Koshti
,
Salil Goel
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.5194/isprs-annals-x-4-2024-37-2024
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.5194/isprs-annals-x-4-2024-37-2024
In recent years, 3D LiDAR (Light Detection and Ranging) has become a crucial sensor in various applications such as autonomous vehicles, robotics, object detection, precision forestry, and agriculture. However, specific LiDAR sensors, such as Velodyne VLP16, exhibit some drawbacks, such as a limited field of view and sparse data density, making them inadequate for certain specific applications. Hence, this research proposes a method for calibrating two VLP16 LiDAR sensors to improve coverage and reduce blind spots. The pipeline for performing the calibration begins with mounting LiDARs correctly in a rod at a specific orientation and distance, followed by the selection of multiple sites for data collection, then performing a registration algorithm for estimating calibration parameters, and then an accuracy assessment of the calibrated point clouds. The registration algorithm used here is a modified version of ICP (Iterative Closest Point), which overcomes the need for initialization and eliminates manual intervention in installing targets or retro-reflectors. Finally, we evaluated the accuracy of the fused point cloud collected in an open environment using two calibrated Velodyne VLP16 sensors. For accuracy assessment, we used the PCA eigenvalue and RMSE value to observe how tightly point clouds are fused. As a calibration result, we got the orientation and translation parameters, which are used to achieve the common coordinate system, and accomplished calibration accuracy up to single-digit precision from all the experimental sites. Now the system of two calibrated VLP16 sensors will provide higher coverage and increased data density and might be useful for forest applications.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.5194/isprs-annals-x-4-2024-37-2024
- OA Status
- diamond
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403532429
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4403532429Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5194/isprs-annals-x-4-2024-37-2024Digital Object Identifier
- Title
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Automated Pipeline for Multi-LiDAR Extrinsic Calibration: Experimental Evaluation and Performance AnalysisWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-10-18Full publication date if available
- Authors
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Surbhi Barnwal, Mansi Koshti, Salil GoelList of authors in order
- Landing page
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https://doi.org/10.5194/isprs-annals-x-4-2024-37-2024Publisher landing page
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.5194/isprs-annals-x-4-2024-37-2024Direct OA link when available
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Lidar, Calibration, Pipeline (software), Computer science, Remote sensing, Environmental science, Geology, Statistics, Mathematics, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.Closest | 138 |
| abstract_inverted_index.Point), | 139 |
| abstract_inverted_index.achieve | 210 |
| abstract_inverted_index.certain | 55 |
| abstract_inverted_index.clouds. | 125 |
| abstract_inverted_index.crucial | 13 |
| abstract_inverted_index.density | 243 |
| abstract_inverted_index.exhibit | 37 |
| abstract_inverted_index.improve | 71 |
| abstract_inverted_index.limited | 43 |
| abstract_inverted_index.observe | 188 |
| abstract_inverted_index.provide | 237 |
| abstract_inverted_index.result, | 198 |
| abstract_inverted_index.sensors | 69, 235 |
| abstract_inverted_index.system, | 214 |
| abstract_inverted_index.targets | 152 |
| abstract_inverted_index.tightly | 190 |
| abstract_inverted_index.various | 16 |
| abstract_inverted_index.version | 134 |
| abstract_inverted_index.Finally, | 155 |
| abstract_inverted_index.However, | 29 |
| abstract_inverted_index.Ranging) | 9 |
| abstract_inverted_index.Velodyne | 35, 173 |
| abstract_inverted_index.accuracy | 119, 159, 177, 218 |
| abstract_inverted_index.coverage | 72, 239 |
| abstract_inverted_index.density, | 50 |
| abstract_inverted_index.followed | 97 |
| abstract_inverted_index.modified | 133 |
| abstract_inverted_index.mounting | 85 |
| abstract_inverted_index.multiple | 102 |
| abstract_inverted_index.pipeline | 78 |
| abstract_inverted_index.proposes | 61 |
| abstract_inverted_index.research | 60 |
| abstract_inverted_index.sensors, | 32 |
| abstract_inverted_index.sensors. | 175 |
| abstract_inverted_index.specific | 30, 56, 93 |
| abstract_inverted_index.Abstract. | 0 |
| abstract_inverted_index.Detection | 7 |
| abstract_inverted_index.algorithm | 111, 128 |
| abstract_inverted_index.collected | 165 |
| abstract_inverted_index.correctly | 87 |
| abstract_inverted_index.distance, | 96 |
| abstract_inverted_index.evaluated | 157 |
| abstract_inverted_index.forestry, | 26 |
| abstract_inverted_index.increased | 241 |
| abstract_inverted_index.overcomes | 141 |
| abstract_inverted_index.precision | 25, 222 |
| abstract_inverted_index.robotics, | 22 |
| abstract_inverted_index.selection | 100 |
| abstract_inverted_index.vehicles, | 21 |
| abstract_inverted_index.(Iterative | 137 |
| abstract_inverted_index.assessment | 120 |
| abstract_inverted_index.autonomous | 20 |
| abstract_inverted_index.calibrated | 123, 172, 233 |
| abstract_inverted_index.coordinate | 213 |
| abstract_inverted_index.detection, | 24 |
| abstract_inverted_index.drawbacks, | 39 |
| abstract_inverted_index.eigenvalue | 183 |
| abstract_inverted_index.eliminates | 147 |
| abstract_inverted_index.estimating | 113 |
| abstract_inverted_index.inadequate | 53 |
| abstract_inverted_index.installing | 151 |
| abstract_inverted_index.performing | 80, 108 |
| abstract_inverted_index.assessment, | 178 |
| abstract_inverted_index.calibrating | 65 |
| abstract_inverted_index.calibration | 82, 114, 197, 217 |
| abstract_inverted_index.collection, | 106 |
| abstract_inverted_index.environment | 169 |
| abstract_inverted_index.orientation | 94, 202 |
| abstract_inverted_index.parameters, | 115, 205 |
| abstract_inverted_index.translation | 204 |
| abstract_inverted_index.accomplished | 216 |
| abstract_inverted_index.agriculture. | 28 |
| abstract_inverted_index.applications | 17 |
| abstract_inverted_index.experimental | 226 |
| abstract_inverted_index.intervention | 149 |
| abstract_inverted_index.registration | 110, 127 |
| abstract_inverted_index.single-digit | 221 |
| abstract_inverted_index.applications. | 57, 250 |
| abstract_inverted_index.initialization | 145 |
| abstract_inverted_index.retro-reflectors. | 154 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/15 |
| sustainable_development_goals[0].score | 0.4099999964237213 |
| sustainable_development_goals[0].display_name | Life in Land |
| citation_normalized_percentile.value | 0.29881271 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |