非構造環境における視覚ベースEnd-to-Endロボットナビゲーションのベンチマーク
Bench2FreeAD: A Benchmark for Vision-based End-to-end Navigation in Unstructured Robotic Environments
非構造なロボット環境でのEnd-to-Endナビゲーション向けに、実機とIsaac Simによるデータ収集パイプラインとFreeWorldデータセットを構築し、VADモデルのファインチューニングで有効性を検証したベンチマークを提案。
著者: Yuhang Peng, Sidong Wang, Jihaoyu Yang, Shilong Li, Han Wang, Jiangtao Gong
分類: cs.RO, cs.CV
原文アブストラクト
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.
