Problem
Research questions and friction points this paper is trying to address.
Semantic Ambiguity
Autonomous Navigation
Vision-Language Models
Traversability Analysis
Estimating Semantic Ambiguity via Gaussian Context Distributions for VLM-Driven Traversability Analysis
🤖 AI Summary
研究通过构建高斯上下文分布来解决视觉语言模型在自主导航中的语义模糊问题，提高复杂户外环境下的安全性和可靠性。
📝 Abstract
Autonomous navigation in unstructured environments requires robust scene understanding, yet Vision-Language Models (VLMs) often suffer from semantic ambiguity, where conflicting predictions can lead to dangerous failures. To address this, we present a novel pipeline for vision-based traversability estimation that explicitly models contextual uncertainty. Our approach utilizes Conceptual Anchoring to ground open-vocabulary VLM predictions onto a continuous physical traversability scale. By formulating the model's responses as a Gaussian Context Distribution (GCD), we derive both a dense traversability map and a dense uncertainty map based on the statistical properties of the distribution. Experimental validation on the real-world GOOSE dataset demonstrates that our proposed uncertainty metric effectively correlates with sources of ambiguity, such as visual artifacts and mixed terrain overlap. The method exhibits competitive performance while offering the distinct advantage of providing statistical uncertainty estimates to address semantic ambiguity, enabling safer and more reliable autonomous behavior in complex outdoor settings.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Gaussian Context Distribution
Conceptual Anchoring
semantic ambiguity
traversability analysis
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