Estimation of Missing LiDAR Data for Accurate AGV Localization
This article evaluates several machine learning methods to substitute the missing light detection and ranging data for better spatial localization of industrial automated guided vehicles. Decision trees and ensemble of trees using bagging or boosting techniques have been considered. Also, the <in...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9804721/ |
| _version_ | 1818584446931042304 |
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| author | Arpad Gellert Darius Sarbu Stefan-Alexandru Precup Alexandru Matei Dragos Circa Constantin-Bala Zamfirescu |
| author_facet | Arpad Gellert Darius Sarbu Stefan-Alexandru Precup Alexandru Matei Dragos Circa Constantin-Bala Zamfirescu |
| author_sort | Arpad Gellert |
| collection | DOAJ |
| description | This article evaluates several machine learning methods to substitute the missing light detection and ranging data for better spatial localization of industrial automated guided vehicles. Decision trees and ensemble of trees using bagging or boosting techniques have been considered. Also, the <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-nearest neighbors algorithm was analyzed. Most of the algorithms have been evaluated based on multiple criteria and hyper parameter tuning. The analysis of the results was done in a comparative way, multiple regression evaluation metrics being considered. The experiments have shown that the extreme gradient boosting algorithm provides the best results in terms of performance, but with timing and resource allocation drawbacks. On the other hand, a simple decision tree model seems to give good results if a tradeoff between performance and prediction time must be made. The <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-nearest neighbors algorithm is also performing pretty well, especially because we are experimenting in a static environment. |
| first_indexed | 2024-12-16T08:21:19Z |
| format | Article |
| id | doaj.art-48931f3764934dbb897980fa4689e7b7 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T08:21:19Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-48931f3764934dbb897980fa4689e7b72022-12-21T22:38:07ZengIEEEIEEE Access2169-35362022-01-0110684166842810.1109/ACCESS.2022.31857639804721Estimation of Missing LiDAR Data for Accurate AGV LocalizationArpad Gellert0https://orcid.org/0000-0002-5482-967XDarius Sarbu1Stefan-Alexandru Precup2Alexandru Matei3https://orcid.org/0000-0003-4299-1052Dragos Circa4Constantin-Bala Zamfirescu5https://orcid.org/0000-0003-0128-2436Computer Science and Electrical Engineering Department, Lucian Blaga University of Sibiu, Sibiu, RomaniaComputer Science and Electrical Engineering Department, Lucian Blaga University of Sibiu, Sibiu, RomaniaComputer Science and Electrical Engineering Department, Lucian Blaga University of Sibiu, Sibiu, RomaniaComputer Science and Electrical Engineering Department, Lucian Blaga University of Sibiu, Sibiu, RomaniaComputer Science and Electrical Engineering Department, Lucian Blaga University of Sibiu, Sibiu, RomaniaComputer Science and Electrical Engineering Department, Lucian Blaga University of Sibiu, Sibiu, RomaniaThis article evaluates several machine learning methods to substitute the missing light detection and ranging data for better spatial localization of industrial automated guided vehicles. Decision trees and ensemble of trees using bagging or boosting techniques have been considered. Also, the <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-nearest neighbors algorithm was analyzed. Most of the algorithms have been evaluated based on multiple criteria and hyper parameter tuning. The analysis of the results was done in a comparative way, multiple regression evaluation metrics being considered. The experiments have shown that the extreme gradient boosting algorithm provides the best results in terms of performance, but with timing and resource allocation drawbacks. On the other hand, a simple decision tree model seems to give good results if a tradeoff between performance and prediction time must be made. The <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-nearest neighbors algorithm is also performing pretty well, especially because we are experimenting in a static environment.https://ieeexplore.ieee.org/document/9804721/Automated guided vehicledigital twinLiDARpoint cloud estimation |
| spellingShingle | Arpad Gellert Darius Sarbu Stefan-Alexandru Precup Alexandru Matei Dragos Circa Constantin-Bala Zamfirescu Estimation of Missing LiDAR Data for Accurate AGV Localization IEEE Access Automated guided vehicle digital twin LiDAR point cloud estimation |
| title | Estimation of Missing LiDAR Data for Accurate AGV Localization |
| title_full | Estimation of Missing LiDAR Data for Accurate AGV Localization |
| title_fullStr | Estimation of Missing LiDAR Data for Accurate AGV Localization |
| title_full_unstemmed | Estimation of Missing LiDAR Data for Accurate AGV Localization |
| title_short | Estimation of Missing LiDAR Data for Accurate AGV Localization |
| title_sort | estimation of missing lidar data for accurate agv localization |
| topic | Automated guided vehicle digital twin LiDAR point cloud estimation |
| url | https://ieeexplore.ieee.org/document/9804721/ |
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