构建森林四足机器人卡困多模态数据集，助力提升野外自主性
ForEnt: A Multi-Modal Dataset for Characterizing Quadruped Robot Entrapments in Forest Environments
- 在8个林地采集1.7公里路径，记录69次卡困事件
- 含同步RGB-D、LiDAR、本体感知与第三人称视频数据
- 适合研究机器人卡困检测与鲁棒部署的团队使用
四足机器人在森林生态监测中应用日益广泛，但其自主性常因穿越林地时遭遇植被缠绕等卡困问题而中断。例如，腿被藤蔓缠住会导致失稳甚至翻倒，不仅影响任务执行，还可能损坏硬件。为填补森林环境下卡困行为专用数据集的空白，我们提出了ForEnt，一个基于低成本Unitree Go2四足机器人的多模态数据集，覆盖英国南安普顿公共林地的8个站点。共完成约1.7公里、11个序列的行走，记录69次卡困事件。ForEnt包含时间同步的RGB-D图像、LiDAR扫描、本体感知数据及第三人称视频，可用于分析导致卡困的地形因素，并为卡困检测策略提供可复现的基准测试数据。该数据集有助于推动四足机器人在复杂森林环境中的鲁棒部署。
原文摘要 · Abstract (English)
Legged robots are increasingly deployed in forests for ecological surveying and monitoring, yet their autonomy is often interrupted consequent to the challenges posed in traversing forest environments. Forest entrapments, for example, when a robot's legs are ensnared in vines or other vegetation, result in loss of stability and toppling. Such events not only disrupt the mission and require manual intervention, but also risk damage to the robot hardware. To address the absence of a dedicated dataset to investigate these failure modes in forest environments, we present ForEnt, a multi-modal dataset collected with the low-cost Unitree Go2 quadruped across eight forest sites in the Southampton Common Woodlands, UK. For our dataset, over approximately 1.7 km of traversals in 11 sequences were conducted, yielding 69 recorded entrapment events. ForEnt includes time-synchronized RGB-D images, LiDAR scans, proprioceptive data, and third-person video, enabling analysis of terrain factors contributing to entrapment and providing labeled sensor streams for reproducible benchmarking. By supporting the evaluation of entrapment detection strategies, ForEnt lowers the barrier to developing robust quadruped robot deployments in challenging forest environments.
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