首个面向非结构化环境的机器人视觉导航基准数据集
Bench2FreeAD: A Benchmark for Vision-based End-to-end Navigation in Unstructured Robotic Environments
- 构建真实与仿真双路径数据采集，生成FreeWorld数据集
- 基于VAD模型微调后，导航能力显著提升
- 适合物流与服务机器人导航研究者使用
当前多数端到端（E2E）自动驾驶算法聚焦于结构化交通场景中的标准车辆，缺乏对辅助道路、校园道路及室内等非结构化环境下的机器人导航探索。本文研究非结构化道路环境中的端到端机器人导航问题。首先，提出两条数据采集路径——一条用于真实机器人数据收集，另一条利用Isaac Sim仿真器生成合成数据，共同构建非结构化机器人导航数据集：FreeWorld Dataset。其次，基于该数据集对高效端到端自动驾驶模型VAD进行微调，验证其在非结构化环境中的性能与适应性。结果表明，通过本数据集微调可显著提升端到端模型在非结构化环境中的导航能力。因此，本文首次提出面向非结构化场景的端到端机器人导航数据集，并基于视觉驱动的端到端算法建立基准，推动物流与服务机器人端到端导航技术发展。项目已开源至Github。
原文摘要 · Abstract (English)
Most current end-to-end (E2E) autonomous driving algorithms are built on standard vehicles in structured transportation scenarios, lacking exploration of robot navigation for unstructured scenarios such as auxiliary roads, campus roads, and indoor settings. This paper investigates E2E robot navigation in unstructured road environments. First, we introduce two data collection pipelines - one for real-world robot data and another for synthetic data generated using the Isaac Sim simulator, which together produce an unstructured robotics navigation dataset -- FreeWorld Dataset. Second, we fine-tuned an efficient E2E autonomous driving model -- VAD -- using our datasets to validate the performance and adaptability of E2E autonomous driving models in these environments. Results demonstrate that fine-tuning through our datasets significantly enhances the navigation potential of E2E autonomous driving models in unstructured robotic environments. Thus, this paper presents the first dataset targeting E2E robot navigation tasks in unstructured scenarios, and provides a benchmark based on vision-based E2E autonomous driving algorithms to facilitate the development of E2E navigation technology for logistics and service robots. The project is available on Github.
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