Semantic Landmark Particle Filter for Robot Localisation in Vineyards

• URL: http://arxiv.org/abs/2603.10847v1
• Date: Wed, 11 Mar 2026 14:57:14 GMT
• Title: Semantic Landmark Particle Filter for Robot Localisation in Vineyards
• Abstract summary: parallel crop rows produce nearly identical LiDAR observations, causing geometry-only and vision-based SLAM systems to converge towards incorrect corridors.<n>We present a Semantic Landmark Particle Filter that integrates trunk and pole landmark detections with 2D LiDAR within a probabilistic localisation framework.
• Score: 1.1077058391970922
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reliable localisation in vineyards is hindered by row-level perceptual aliasing: parallel crop rows produce nearly identical LiDAR observations, causing geometry-only and vision-based SLAM systems to converge towards incorrect corridors, particularly during headland transitions. We present a Semantic Landmark Particle Filter (SLPF) that integrates trunk and pole landmark detections with 2D LiDAR within a probabilistic localisation framework. Detected trunks are converted into semantic walls, forming structural row boundaries embedded in the measurement model to improve discrimination between adjacent rows. GNSS is incorporated as a lightweight prior that stabilises localisation when semantic observations are sparse. Field experiments in a 10-row vineyard demonstrate consistent improvements over geometry-only (AMCL), vision-based (RTAB-Map), and GNSS baselines. Compared to AMCL, SLPF reduces Absolute Pose Error by 22% and 65% across two traversal directions; relative to a NoisyGNSS baseline, APE decreases by 65% and 61%. Row correctness improves from 0.67 to 0.73, while mean cross-track error decreases from 1.40 m to 1.26 m. These results show that embedding row-level structural semantics within the measurement model enables robust localisation in highly repetitive outdoor agricultural environments.

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