VinePT-Map: Pole-Trunk Semantic Mapping for Resilient Autonomous Robotics in Vineyards

TL;DR

VinePT-Map is introduced, a semantic mapping framework that leverages vine trunks and support poles as persistent structural landmarks to enable season-agnostic and resilient robot localization and is suitability for long-term autonomous operation in agricultural environments.

Abstract

Reliable long-term deployment of autonomous robots in agricultural environments remains challenging due to perceptual aliasing, seasonal variability, and the dynamic nature of crop canopies. Vineyards, characterized by repetitive row structures and significant visual changes across phenological stages, represent a pivotal field challenge, limiting the robustness of conventional feature-based localization and mapping approaches. This paper introduces VinePT-Map, a semantic mapping framework that leverages vine trunks and support poles as persistent structural landmarks to enable season-agnostic and resilient robot localization. The proposed method formulates the mapping problem as a factor graph, integrating GPS, IMU, and RGB-D observations through robust geometrical constraints that exploit vineyard structure. An efficient perception pipeline based on instance segmentation and tracking, combined with a clustering filter for outlier rejection and pose refinement, enables accurate landmark detection using low-cost sensors and onboard computation. To validate the pipeline, we present a multi-season dataset for trunk and pole segmentation and tracking. Extensive field experiments conducted across diverse seasons demonstrate the robustness and accuracy of the proposed approach, highlighting its suitability for long-term autonomous operation in agricultural environments.

Figures (6)

• Figure 1: VinePT-Map enables robotic platforms to build permanent landmarks map in vineyards for robust autonomous operation in all the seasons.
• Figure 2: VinePT-Map: a schematic pipeline of the semantic mapping framework for persistent landmarks in vineyards.
• Figure 3: VinePT-Map Factor Graph for poles and trunks mapping.
• Figure 4: Aerial view of the vineyard with the trajectory of the robot over three rows (starting from dark line, ending in yellow line), used to compute the incremental result of Table \ref{['tab:table_map2']}.
• Figure 5: Box plots of the ablation study results on poles mapping error. The relevance of the landmark reference point computation method (rd) and the data association method (rc) is highlighted by excluding them from the pipeline.