# Semantic Landmark Particle Filter for Robot Localisation in Vineyards

arXiv: https://arxiv.org/abs/2603.10847 · HTML: https://mlsanity.com/paper/2603.10847/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 24 references that are in the map.
- First posted on arXiv 11 Mar 2026.

## Builds on (most cited first, 5 of 5)

- [YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information](https://mlsanity.com/paper/2402.13616/) (2024)
- [RTAB-Map as an Open-Source Lidar and Visual SLAM Library for Large-Scale and Long-Term Online Operation](https://mlsanity.com/paper/2403.06341/) (2024)
- [Artificial Intelligence for Long-Term Robot Autonomy: A Survey](https://mlsanity.com/paper/1807.05196/) (2018)
- [Benchmark of visual and 3D lidar SLAM systems in simulation environment for vineyards](https://mlsanity.com/paper/2107.05283/) (2021)
- [Keypoint Semantic Integration for Improved Feature Matching in Outdoor Agricultural Environments](https://mlsanity.com/paper/2503.08843/) (2025)

## Abstract (arXiv)

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.