Advanced techniques and applications of LiDAR Place Recognition in Agricultural Environments: A Comprehensive Survey
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866911408458825728 |
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| author | Vilella-Cantos, Judith Ballesta, Mónica Valiente, David Flores, María Payá, Luis |
| author_facet | Vilella-Cantos, Judith Ballesta, Mónica Valiente, David Flores, María Payá, Luis |
| contents | An optimal solution to the localization problem is essential for developing autonomous robotic systems. Apart from autonomous vehicles, precision agriculture is one of the elds that can bene t most from these systems. Although LiDAR place recognition is a widely used technique in recent years to achieve accurate localization, it is mostly used in urban settings. However, the lack of distinctive features and the unstructured nature of agricultural environments make place recognition challenging. This work presents a comprehensive review of state-of-the-art the latest deep learning applications for agricultural environments and LPR techniques. We focus on the challenges that arise in these environments. We analyze the existing approaches, datasets, and metrics used to evaluate LPR system performance and discuss the limitations and future directions of research in this eld. This is the rst survey that focuses on LiDAR based localization in agricultural settings, with the aim of providing a thorough understanding and fostering further research in this specialized domain. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_22198 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Advanced techniques and applications of LiDAR Place Recognition in Agricultural Environments: A Comprehensive Survey Vilella-Cantos, Judith Ballesta, Mónica Valiente, David Flores, María Payá, Luis Robotics Artificial Intelligence Emerging Technologies An optimal solution to the localization problem is essential for developing autonomous robotic systems. Apart from autonomous vehicles, precision agriculture is one of the elds that can bene t most from these systems. Although LiDAR place recognition is a widely used technique in recent years to achieve accurate localization, it is mostly used in urban settings. However, the lack of distinctive features and the unstructured nature of agricultural environments make place recognition challenging. This work presents a comprehensive review of state-of-the-art the latest deep learning applications for agricultural environments and LPR techniques. We focus on the challenges that arise in these environments. We analyze the existing approaches, datasets, and metrics used to evaluate LPR system performance and discuss the limitations and future directions of research in this eld. This is the rst survey that focuses on LiDAR based localization in agricultural settings, with the aim of providing a thorough understanding and fostering further research in this specialized domain. |
| title | Advanced techniques and applications of LiDAR Place Recognition in Agricultural Environments: A Comprehensive Survey |
| topic | Robotics Artificial Intelligence Emerging Technologies |
| url | https://arxiv.org/abs/2601.22198 |