SOURCE-LINKED INTELLIGENCE

Estimating Semantic Ambiguity via Gaussian Context Distributions for VLM-Driven Traversability Analysis

arXiv · AI, language, vision and robotics · article · Sep 8, 2026 · UTC

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 travers

Read original source ↗ Open in workspace

recordType

paper

region

Global