AI RESEARCH
Semantic Landmark Particle Filter for Robot Localisation in Vineyards
arXiv CS.AI
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ArXi:2603.10847v1 Announce Type: cross 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.