Semantic-Aware Particle Filter for Reliable Vineyard Robot Localisation

• URL: http://arxiv.org/abs/2509.18342v1
• Date: Mon, 22 Sep 2025 19:04:31 GMT
• Title: Semantic-Aware Particle Filter for Reliable Vineyard Robot Localisation
• Abstract summary: Localisation is critical for mobile robots in structured outdoor environments, yet LiDAR-based methods often fail in vineyards.<n>We propose a semantic particle filter that incorporates stable object-level detections, specifically vine trunks.<n>Semantic walls connect adjacent landmarks into pseudo-structural constraints that mitigate row aliasing.<n>Our approach maintains localisation within the correct row, recovers from deviations where AMCL fails, and outperforms vision-based SLAM methods such as RTAB-Map.
• Score: 4.0016757648498205
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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