Global self-localization and navigation using 3D-LiDAR for headland turning in vineyards

When using the real-time kinematic global navigation satellite system (RTK-GNSS) in orchards or vineyards, autonomous navigation can be disrupted due to the inability to obtain ambiguity-fixed solutions. In this study, we employed light detection and ranging in a three-dimensional space (3D-LiDAR) f...

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Main Authors: Kanya Usu, Yoshitomo Yamasaki, Kazunobu Ishii, Noboru Noguchi
Format: Article
Language:English
Published: Elsevier 2025-12-01
Series:Smart Agricultural Technology
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Online Access:http://www.sciencedirect.com/science/article/pii/S2772375525005271
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author Kanya Usu
Yoshitomo Yamasaki
Kazunobu Ishii
Noboru Noguchi
author_facet Kanya Usu
Yoshitomo Yamasaki
Kazunobu Ishii
Noboru Noguchi
author_sort Kanya Usu
collection DOAJ
description When using the real-time kinematic global navigation satellite system (RTK-GNSS) in orchards or vineyards, autonomous navigation can be disrupted due to the inability to obtain ambiguity-fixed solutions. In this study, we employed light detection and ranging in a three-dimensional space (3D-LiDAR) for global self-localization only during headland turning in a vineyard without RTK-GNSS in real time. The RTK-GNSS was only used in advance to create a map of structural poles. The 3D-LiDAR information was used to detect and track poles at the ends of rows of grapevines in headlands. The global self-localization was conducted based on the distance between the poles and the vehicle, combined with pole positions obtained in advance via RTK-GNSS. Experimental tests were conducted through growing seasons in a commercial vineyard by two different vehicles at two different sites. The results showed a 0.101 m root mean square error (RMSE) between the estimated position and the ground truth acquired through the RTK-GNSS. We discussed the RMSE by calculating horizontal dilution of precision (HDOP), and it was found that the accuracy was improved to 0.091 m on average under the condition that the HDOP was less than 20. An algorithm for robust localization, missing pole detection, was suggested and verified to improve accuracy by 14 %. Additionally, the travel results revealed a 0.075 m lateral RMSE between the trajectory navigated by the RTK-GNSS and that by the 3D-LiDAR. These findings suggest that using 3D-LiDAR as a navigation sensor can serve as a viable alternative to RTK-GNSS for the turning in headlands of autonomous vehicles in vineyards.
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spelling doaj-art-2be1d37275d4490c9fd2ca19b2b418da2025-08-20T03:36:06ZengElsevierSmart Agricultural Technology2772-37552025-12-011210129610.1016/j.atech.2025.101296Global self-localization and navigation using 3D-LiDAR for headland turning in vineyardsKanya Usu0Yoshitomo Yamasaki1Kazunobu Ishii2Noboru Noguchi3Graduate School of Agriculture, Hokkaido University, Kita-9, Nishi-9, Kita-Ku, Sapporo, Hokkaido, 060-8589, JapanResearch Faculty of Agriculture, Hokkaido University, Kita-9, Nishi-9, Kita-Ku, Sapporo, Hokkaido, 060-8589, Japan; Corresponding author.Research Faculty of Agriculture, Hokkaido University, Kita-9, Nishi-9, Kita-Ku, Sapporo, Hokkaido, 060-8589, JapanResearch Faculty of Agriculture, Hokkaido University, Kita-9, Nishi-9, Kita-Ku, Sapporo, Hokkaido, 060-8589, JapanWhen using the real-time kinematic global navigation satellite system (RTK-GNSS) in orchards or vineyards, autonomous navigation can be disrupted due to the inability to obtain ambiguity-fixed solutions. In this study, we employed light detection and ranging in a three-dimensional space (3D-LiDAR) for global self-localization only during headland turning in a vineyard without RTK-GNSS in real time. The RTK-GNSS was only used in advance to create a map of structural poles. The 3D-LiDAR information was used to detect and track poles at the ends of rows of grapevines in headlands. The global self-localization was conducted based on the distance between the poles and the vehicle, combined with pole positions obtained in advance via RTK-GNSS. Experimental tests were conducted through growing seasons in a commercial vineyard by two different vehicles at two different sites. The results showed a 0.101 m root mean square error (RMSE) between the estimated position and the ground truth acquired through the RTK-GNSS. We discussed the RMSE by calculating horizontal dilution of precision (HDOP), and it was found that the accuracy was improved to 0.091 m on average under the condition that the HDOP was less than 20. An algorithm for robust localization, missing pole detection, was suggested and verified to improve accuracy by 14 %. Additionally, the travel results revealed a 0.075 m lateral RMSE between the trajectory navigated by the RTK-GNSS and that by the 3D-LiDAR. These findings suggest that using 3D-LiDAR as a navigation sensor can serve as a viable alternative to RTK-GNSS for the turning in headlands of autonomous vehicles in vineyards.http://www.sciencedirect.com/science/article/pii/S2772375525005271Headland turningGNSS-free positioningVineyard navigationPrecision viticulture
spellingShingle Kanya Usu
Yoshitomo Yamasaki
Kazunobu Ishii
Noboru Noguchi
Global self-localization and navigation using 3D-LiDAR for headland turning in vineyards
Smart Agricultural Technology
Headland turning
GNSS-free positioning
Vineyard navigation
Precision viticulture
title Global self-localization and navigation using 3D-LiDAR for headland turning in vineyards
title_full Global self-localization and navigation using 3D-LiDAR for headland turning in vineyards
title_fullStr Global self-localization and navigation using 3D-LiDAR for headland turning in vineyards
title_full_unstemmed Global self-localization and navigation using 3D-LiDAR for headland turning in vineyards
title_short Global self-localization and navigation using 3D-LiDAR for headland turning in vineyards
title_sort global self localization and navigation using 3d lidar for headland turning in vineyards
topic Headland turning
GNSS-free positioning
Vineyard navigation
Precision viticulture
url http://www.sciencedirect.com/science/article/pii/S2772375525005271
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