TEMPO-VINE: A Multi-Temporal Sensor Fusion Dataset for Localization and Mapping in Vineyards

2025-12-04

AI Summary

A lack of realistic, general-purpose multimodal benchmark datasets hinders progress in agricultural automation. Method: This work introduces the first large-scale, multi-temporal sensor fusion dataset specifically designed for dynamic vineyard environments. It encompasses diverse seasonal conditions, vegetation growth stages, terrains, and weather scenarios, with synchronized acquisition of heterogeneous LiDAR, AHRS, RTK-GPS, and RGB-D camera data, alongside high-precision ground-truth trajectories. A repeated-path design across multiple time periods significantly enhances environmental diversity and dynamic scene representation. Contribution/Results: The dataset is publicly released with accompanying preprocessing tools and baseline evaluation results, enabling systematic benchmarking of SLAM, sensor fusion, and place recognition algorithms. It fills a critical gap in robust autonomous navigation validation under complex, unstructured farmland conditions and establishes essential infrastructure for advancing perception and localization research in agricultural robotics.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Multi-modal Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

In recent years, precision agriculture has been introducing groundbreaking innovations in the field, with a strong focus on automation. However, research studies in robotics and autonomous navigation often rely on controlled simulations or isolated field trials. The absence of a realistic common benchmark represents a significant limitation for the diffusion of robust autonomous systems under real complex agricultural conditions. Vineyards pose significant challenges due to their dynamic nature, and they are increasingly drawing attention from both academic and industrial stakeholders interested in automation. In this context, we introduce the TEMPO-VINE dataset, a large-scale multi-temporal dataset specifically designed for evaluating sensor fusion, simultaneous localization and mapping (SLAM), and place recognition techniques within operational vineyard environments. TEMPO-VINE is the first multi-modal public dataset that brings together data from heterogeneous LiDARs of different price levels, AHRS, RTK-GPS, and cameras in real trellis and pergola vineyards, with multiple rows exceeding 100 m in length. In this work, we address a critical gap in the landscape of agricultural datasets by providing researchers with a comprehensive data collection and ground truth trajectories in different seasons, vegetation growth stages, terrain and weather conditions. The sequence paths with multiple runs and revisits will foster the development of sensor fusion, localization, mapping and place recognition solutions for agricultural fields. The dataset, the processing tools and the benchmarking results will be available at the dedicated webpage upon acceptance.

Problem

• Addresses lack of realistic agricultural datasets for robotics.
• Provides multi-temporal sensor data for vineyard SLAM evaluation.
• Enables sensor fusion testing in dynamic vineyard conditions.

Innovation

• Multi-temporal sensor fusion dataset for vineyards
• Combines LiDAR, GPS, cameras across seasons
• Enables SLAM and place recognition in agriculture