Generative Diffusion Model-Assisted Efficient Fingerprinting for In-Orchard Localization

Abstract

Precise robot localization at the tree level is essential for smart agriculture applications such as precision disease management and targeted nutrient distribution. Existing methods fail to achieve the required accuracy. We propose OrchLoc, a fingerprinting-based localization solution that achieves treelevel precision using a single Long Range (LoRa) gateway. Our approach utilizes channel state information (CSI) across eight channels as a localization fingerprint. To minimize labor-intensive site surveys for fingerprint database construction and maintenance, we develop a CSI generative model (CGM) that learns the relationship between CSI vectors and their corresponding locations. The CGM is fine-tuned using CSI data from static agricultural LoRa sensor nodes, enabling continuous fingerprint database updates. Extensive experiments in two orchards demonstrate that OrchLoc effectively achieves accurate tree-level localization with minimal overhead, improving robot navigation