TL;DR
This paper introduces a biologically inspired, computationally efficient teach and repeat navigation method that leverages odometry and lightweight visual processing, demonstrating robustness across different robots and challenging environments.
Contribution
A novel asynchronous teach and repeat navigation framework that uses odometry and lightweight visual correction, enabling cross-platform robustness with low computational requirements.
Findings
Successfully navigated over 6000 meters in diverse environments.
Outperformed state-of-the-art systems in low-resolution and lighting-changed conditions.
Enabled cross-platform route learning and repetition without parameter adjustments.
Abstract
Fully autonomous mobile robots have a multitude of potential applications, but guaranteeing robust navigation performance remains an open research problem. For many tasks such as repeated infrastructure inspection, item delivery, or inventory transport, a route repeating capability can be sufficient and offers potential practical advantages over a full navigation stack. Previous teach and repeat research has achieved high performance in difficult conditions predominantly by using sophisticated, expensive sensors, and has often had high computational requirements. Biological systems, such as small animals and insects like seeing ants, offer a proof of concept that robust and generalisable navigation can be achieved with extremely limited visual systems and computing power. In this work we create a novel asynchronous formulation for teach and repeat navigation that fully utilises odometry…
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