Accurate localisation is critical for mobile robots in structured outdoor environments, yet LiDAR-based methods often fail in vineyards due to repetitive row geometry and perceptual aliasing. We propose a semantic particle filter that incorporates stable object-level detections, specifically vine trunks and support poles into the likelihood estimation process. Detected landmarks are projected into a birds eye view and fused with LiDAR scans to generate semantic observations. A key innovation is the use of semantic walls, which connect adjacent landmarks into pseudo-structural constraints that mitigate row aliasing. To maintain global consistency in headland regions where semantics are sparse, we introduce a noisy GPS prior that adaptively supports the filter. Experiments in a real vineyard demonstrate that our approach maintains localisation within the correct row, recovers from deviations where AMCL fails, and outperforms vision-based SLAM methods such as RTAB-Map.
@article{arxiv.2509.18342,
title = {Semantic-Aware Particle Filter for Reliable Vineyard Robot Localisation},
author = {Rajitha de Silva and Jonathan Cox and James R. Heselden and Marija Popovic and Cesar Cadena and Riccardo Polvara},
journal= {arXiv preprint arXiv:2509.18342},
year = {2025}
}