FORWARD: Dataset of a forwarder operating in rough terrain
🤖 AI Summary
To address key challenges in terrain traversability assessment, environmental perception, and autonomous control of forestry machinery operating in rugged terrain, this study introduces the first high-precision, multimodal dataset specifically designed for timber extraction operations in forested environments. Leveraging synchronized data acquisition from RTK-GNSS, 360° panoramic vision, IMU, CAN bus, vibration sensors, and high-density LiDAR-based terrain scanning, we collected 18 hours of real-world operational data across diverse Swedish forest sites—covering varying payload conditions, vehicle speeds, and track configurations. The dataset provides centimeter-level georeferencing, standardized Stanford-style logging formats, and fine-grained annotations of operational primitives. It represents the first long-duration, multi-scenario, high spatiotemporal-resolution record of end-to-end forestry machinery operations, enabling advances in traversability modeling, energy consumption optimization, autonomous navigation algorithm development, and digital twin simulation. This resource establishes a foundational data infrastructure and methodological framework for intelligent forestry equipment research and development.
Application Category
📝 Abstract
We present FORWARD, a high-resolution multimodal dataset of a cut-to-length forwarder operating in rough terrain on two harvest sites in the middle part of Sweden. The forwarder is a large Komatsu model equipped with a variety of sensors, including RTK-GNSS, 360-camera, operator vibration sensors, internal CAN-bus signal recording, and multiple IMUs. The data includes event time logs recorded in 5 Hz with e.g., driving speed, fuel consumption, vehicle position with centimeter accuracy, and crane use while the vehicle operates in forest areas laser-scanned with very high-resolution, $sim$1500 points per square meter. Production log files (StanForD standard) with time-stamped machine events, extensive video material, and terrain data in various formats are included as well. About 18 hours of regular wood extraction work during three days is annotated from 360-video material into individual work elements and included in the dataset. We also include scenario specifications of conducted experiments on forest roads and in terrain. Scenarios include repeatedly driving the same routes with and without steel tracks, different load weight, and different target driving speeds. The dataset is intended for developing models and algorithms for trafficability, perception, and autonomous control of forest machines using artificial intelligence, simulation, and experiments on physical testbeds. In part, we focus on forwarders traversing terrain, avoiding obstacles, and loading or unloading logs, with consideration for efficiency, fuel consumption, safety, and environmental impact. Other benefits of the open dataset include the ability to explore auto-generation and calibration of forestry machine simulators and automation scenario descriptions using the data recorded in the field.
Problem
Research questions and friction points this paper is trying to address.
Developing AI models for autonomous forest machine navigation in rough terrain
Optimizing forwarder efficiency and fuel consumption during wood extraction
Creating simulation tools for forestry equipment testing and calibration
Innovation
Methods, ideas, or system contributions that make the work stand out.
High-resolution multimodal dataset with RTK-GNSS and sensors
Includes annotated work elements from 360-video recordings
Enables AI models for autonomous forest machine navigation
🔎 Similar Papers
No similar papers found.
