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 vehicle telematics sensors, including global positioning via satellite navigation, movement sensors, accelerometers, and engine sensors. The forwarder was additionally equipped with cameras, operator vibration sensors, and multiple IMUs. The data includes event time logs recorded at 5 Hz of driving speed, fuel consumption, machine position with centimeter accuracy, and crane use while the forwarder operates in forest areas, aerially laser-scanned with a resolution of around 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 weights, 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 or handling 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.

翻译：本文介绍FORWARD数据集，该数据集包含一台在瑞典中部两个采伐点崎岖地形中运行的造材-集材联合机的高分辨率多模态数据。该集材机为大型小松机型，配备车辆遥测传感器，包括基于卫星导航的全球定位系统、运动传感器、加速度计及发动机传感器。此外，集材机还加装了摄像头、操作员振动传感器和多个惯性测量单元（IMU）。数据包含以5Hz频率记录的事件时间日志，涵盖行驶速度、燃油消耗、厘米级精度的机器位置以及集材机在森林区域作业时的起重机使用情况——该区域已通过航空激光扫描获取分辨率约1500点/平方米的点云数据。同时收录了带有时间戳机器事件的斯坦福标准格式生产日志文件、大量视频材料及多种格式的地形数据。基于360度视频材料，对三天内约18小时常规木材采伐作业进行了逐工作要素标注并纳入数据集。此外还包含在森林道路和地形中开展的实验场景规范，包括在有无钢制履带、不同负载重量及不同目标行驶速度条件下重复行驶相同路线等场景。该数据集旨在利用人工智能、仿真及物理实验平台，开发用于森林机械的通过性评估、环境感知及自主控制的模型与算法。研究重点包括集材机穿越地形、规避或处理障碍物、装卸原木等操作，同时兼顾效率、燃油消耗、安全性及环境影响。该开放数据集的其他价值在于，可利用现场记录数据探索林业机械仿真器的自动生成与标定，以及自动化场景描述的构建。