Watching Grass Grow: Long-term Visual Navigation and Mission Planning for Autonomous Biodiversity Monitoring

TL;DR

This paper presents a long-term visual navigation system for autonomous robots in dynamic grassland ecosystems, enabling biodiversity monitoring over weeks despite visual ambiguity and seasonal changes.

Contribution

It introduces a robust, long-term visual navigation pipeline tailored for complex, dynamic natural environments, demonstrated through a 6-week biodiversity monitoring deployment.

Findings

Successful 6-week autonomous monitoring of grass species

Navigation system maintains robustness amid seasonal vegetation changes

Pipeline adaptable to various complex natural settings

Abstract

We describe a challenging robotics deployment in a complex ecosystem to monitor a rich plant community. The study site is dominated by dynamic grassland vegetation and is thus visually ambiguous and liable to drastic appearance change over the course of a day and especially through the growing season. This dynamism and complexity in appearance seriously impact the stability of the robotics platform, as localisation is a foundational part of that control loop, and so routes must be carefully taught and retaught until autonomy is robust and repeatable. Our system is demonstrated over a 6-week period monitoring the response of grass species to experimental climate change manipulations. We also discuss the applicability of our pipeline to monitor biodiversity in other complex natural settings.