A Map-Free LiDAR-Based System for Autonomous Navigation in Vineyards

TL;DR

This paper presents a LiDAR-based autonomous navigation system for vineyards that leverages the geometric structure of rows, achieving high accuracy in both simulated and real environments, including end-row navigation.

Contribution

The novel system uses LiDAR and wheel encoders for precise in-row and end-row navigation in vineyards, addressing a gap in existing agricultural robot navigation methods.

Findings

Achieves mean displacement errors of 0.049 m in simulation and 0.372 m in real environments.

Successfully performs in-row and end-row navigation in vineyards.

Validated effectiveness through tests in simulated and real vineyard environments.

Abstract

Agricultural robots have the potential to increase production yields and reduce costs by performing repetitive and time-consuming tasks. However, for robots to be effective, they must be able to navigate autonomously in fields or orchards without human intervention. In this paper, we introduce a navigation system that utilizes LiDAR and wheel encoder sensors for in-row, turn, and end-row navigation in row structured agricultural environments, such as vineyards. Our approach exploits the simple and precise geometrical structure of plants organized in parallel rows. We tested our system in both simulated and real environments, and the results demonstrate the effectiveness of our approach in achieving accurate and robust navigation. Our navigation system achieves mean displacement errors from the center line of 0.049 m and 0.372 m for in-row navigation in the simulated and real environments, respectively. In addition, we developed an end-row points detection that allows end-row navigation in vineyards, a task often ignored by most works.