Advanced techniques and applications of LiDAR Place Recognition in Agricultural Environments: A Comprehensive Survey

TL;DR

This comprehensive survey reviews recent deep learning-based LiDAR place recognition techniques tailored for agricultural environments, addressing unique challenges and highlighting future research directions in this specialized domain.

Contribution

It is the first survey focusing on LiDAR-based localization in agricultural settings, analyzing current approaches, datasets, and evaluation metrics.

Findings

Deep learning enhances LiDAR place recognition accuracy in agriculture.

Agricultural environments pose unique challenges for LiDAR-based localization.

The survey identifies gaps and suggests future research directions.

Abstract

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.