Semantic-Aware Particle Filter for Reliable Vineyard Robot Localisation
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914051414556672 |
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| author | de Silva, Rajitha Cox, Jonathan Heselden, James R. Popovic, Marija Cadena, Cesar Polvara, Riccardo |
| author_facet | de Silva, Rajitha Cox, Jonathan Heselden, James R. Popovic, Marija Cadena, Cesar Polvara, Riccardo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_18342 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Semantic-Aware Particle Filter for Reliable Vineyard Robot Localisation de Silva, Rajitha Cox, Jonathan Heselden, James R. Popovic, Marija Cadena, Cesar Polvara, Riccardo Robotics Computer Vision and Pattern Recognition 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. |
| title | Semantic-Aware Particle Filter for Reliable Vineyard Robot Localisation |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.18342 |