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Semantic Landmark Particle Filter for Robot Localisation in Vineyards

Rajitha de Silva, Jonathan Cox, James R. Heselden, Marija Popović, Cesar Cadena, Riccardo Polvara

TL;DR

A Semantic Landmark Particle Filter (SLPF) is presented that integrates trunk and pole landmark detections with 2D LiDAR within a probabilistic localisation framework and enables robust localisation in highly repetitive outdoor agricultural environments.

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.

Semantic Landmark Particle Filter for Robot Localisation in Vineyards

TL;DR

A Semantic Landmark Particle Filter (SLPF) is presented that integrates trunk and pole landmark detections with 2D LiDAR within a probabilistic localisation framework and enables robust localisation in highly repetitive outdoor agricultural environments.

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.
Paper Structure (31 sections, 14 equations, 5 figures, 3 tables)

This paper contains 31 sections, 14 equations, 5 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Semantic Particle Filter Localisation in Vineyards
  4. Semantic Landmark Detection and BEV Projection
  5. Row-aligned Structural Map
  6. Particle Filter Formulation
  7. Motion Model
  8. Ray Casting Prediction
  9. Likelihood Estimation
  10. Semantic and Background Rays
  11. GNSS Prior
  12. Corridor Prior
  13. Robust Fusion and Adaptive Weighting
  14. Resampling and pose estimation
  15. Computational Considerations
  16. ...and 16 more sections

Figures (5)

  • Figure 1: Vineyard semantics detection (left) projected onto the landmark map (right) for likelihood calculation. Green: vine trunks, Blue: support poles.
  • Figure 2: Vineyard structural map with RTK-GNSS ground-truth traverses for the two experiments. Orange circles and green squares indicate row posts and vines, while orange and green line segments denote pole- and trunk-based semantic walls. Red (Experiment 1) and blue (Experiment 2) trajectories are the RTK paths; start/end markers indicate traversal direction.
  • Figure 3: Overview of the Semantic Particle Filter. Instance masks from segmentation are combined with depth to generate BEV projections of vineyard landmarks. These projections label near-field LiDAR returns as semantic observations for the particle filter, augmented with a NoisyGNSS prior.
  • Figure 4: Raw Absolute Pose Error (APE) trajectories comparing SLPF (ours), AMCL+NoisyGNSS (Kalman fusion), RTAB-Map RGBD, and NoisyGNSS (subsampled to facilitate visualisation). The dashed black line is ground truth; coloured paths show method deviations.
  • Figure 5: Zoomed comparison during a headland transition (Experiment 1). AMCL+NoisyGNSS remains aligned to an adjacent row despite locally smooth tracking, while SLPF recovers through semantic wall constraints and adaptive GNSS weighting.