Autonomous Navigation in Rows of Trees and High Crops with Deep Semantic Segmentation

Abstract

Segmentation-based autonomous navigation has recently been proposed as a promising methodology to guide robotic platforms through crop rows without requiring precise GPS localization. However, existing methods are limited to scenarios where the centre of the row can be identified thanks to the sharp distinction between the plants and the sky. However, GPS signal obstruction mainly occurs in the case of tall, dense vegetation, such as high tree rows and orchards. In this work, we extend the segmentation-based robotic guidance to those scenarios where canopies and branches occlude the sky and hinder the usage of GPS and previous methods, increasing the overall robustness and adaptability of the control algorithm. Extensive experimentation on several realistic simulated tree fields and vineyards demonstrates the competitive advantages of the proposed solution.

References in corpus (7)

• Position-Agnostic Autonomous Navigation in Vineyards with Deep Reinforcement Learning
• Marvin: an Innovative Omni-Directional Robotic Assistant for Domestic Environments
• Deep Semantic Segmentation at the Edge for Autonomous Navigation in Vineyard Rows
• Waypoint Generation in Row-based Crops with Deep Learning and Contrastive Clustering
• A Deep Learning Driven Algorithmic Pipeline for Autonomous Navigation in Row-Based Crops
• Back-to-Bones: Rediscovering the Role of Backbones in Domain Generalization
• Domain Generalization for Crop Segmentation with Standardized Ensemble Knowledge Distillation