用视觉语言模型实现零样本地形可通行性估计，探索其可行性和挑战
Towards Zero-Shot Terrain Traversability Estimation: Challenges and Opportunities
- 基于人类标注数据构建小规模可通行性评估集
- 当前基础模型在零样本下表现不稳定，但具研究价值
- 适合对自主导航和多模态感知感兴趣的开发者
地形可通行性估计对自主机器人至关重要，尤其在非结构化环境中，视觉线索与推理能力尤为关键。尽管视觉语言模型（VLMs）为零样本估计提供了可能，但该问题本质上仍属不适定。为此，我们引入一个由人工标注的水体可通行性评分小数据集，结果显示尽管判断存在主观性，但人类评估者间仍有一定共识。此外，我们提出一种简单流水线，整合VLM实现零样本可通行性估计。实验表明结果参差不齐，暗示当前基础模型尚不适合实际部署，但为后续研究提供了宝贵洞见。
原文摘要 · Abstract (English)
Terrain traversability estimation is crucial for autonomous robots, especially in unstructured environments where visual cues and reasoning play a key role. While vision-language models (VLMs) offer potential for zero-shot estimation, the problem remains inherently ill-posed. To explore this, we introduce a small dataset of human-annotated water traversability ratings, revealing that while estimations are subjective, human raters still show some consensus. Additionally, we propose a simple pipeline that integrates VLMs for zero-shot traversability estimation. Our experiments reveal mixed results, suggesting that current foundation models are not yet suitable for practical deployment but provide valuable insights for further research.
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