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

2026-03-05

AI Summary

This work proposes a season-invariant semantic mapping framework for agricultural environments, addressing the challenge of degraded long-term localization and mapping robustness caused by repetitive structures, seasonal variations, and dynamic crop growth. By treating grapevine trunks and support poles as semantic primitives, the method integrates instance segmentation, clustering-based denoising, and multi-sensor (GPS/IMU/RGB-D) factor graph optimization, enhanced with geometric constraints and structural priors. This enables high-precision, season-agnostic autonomous localization using low-cost sensors. Extensive field experiments across multiple seasons demonstrate the system’s robustness, and the authors release the first multi-season dataset dedicated to trunk and pole segmentation and tracking.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Computer Vision: Remote Sensing / Geospatial AI
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

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.

Problem

• perceptual aliasing
• seasonal variability
• dynamic crop canopies
• vineyard robotics
• long-term autonomy

Innovation

• semantic mapping
• vineyard robotics
• structural landmarks
• factor graph optimization
• instance segmentation