Improving the ROS 2 Navigation Stack with Real-Time Local Costmap Updates for Agricultural Applications
Ettore Sani, Antonio Sgorbissa, Stefano Carpin

TL;DR
This paper introduces a lightweight, real-time method to improve outdoor robot navigation in agricultural environments by dynamically updating local costmaps using depth camera data, enabling better obstacle handling.
Contribution
It presents a novel system that injects pixel-level classification corrections into costmaps in real time, enhancing Nav2's outdoor navigation capabilities.
Findings
Enables robots to navigate through tall grass and weeds safely.
Improves navigation efficiency in outdoor agricultural environments.
Validated on a Clearpath Husky with successful real-world tests.
Abstract
The ROS 2 Navigation Stack (Nav2) has emerged as a widely used software component providing the underlying basis to develop a variety of high-level functionalities. However, when used in outdoor environments such as orchards and vineyards, its functionality is notably limited by the presence of obstacles and/or situations not commonly found in indoor settings. One such example is given by tall grass and weeds that can be safely traversed by a robot, but that can be perceived as obstacles by LiDAR sensors, and then force the robot to take longer paths to avoid them, or abort navigation altogether. To overcome these limitations, domain specific extensions must be developed and integrated into the software pipeline. This paper presents a new, lightweight approach to address this challenge and improve outdoor robot navigation. Leveraging the multi-scale nature of the costmaps supporting…
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Taxonomy
TopicsSmart Agriculture and AI
