Vision language model improves uncertainty estimates in robot pathfinding
Estimating Semantic Ambiguity via Gaussian Context Distributions for VLM-Driven Traversability Analysis
Robotics
Summary
Robots need to understand their surroundings well to move safely, but sometimes their vision and language models get confused about what objects or terrains they see. The authors developed a new technique that helps the robot estimate how certain or uncertain it is about what it sees by representing it as a kind of statistical distribution. This method creates detailed maps showing not only where the robot can go but also where the robot is unsure, making navigation safer. Their tests on a real-world dataset show this approach works well and highlights tricky situations like mixed ground types or visual glitches.
What this means in practice
- •For robotics engineers: Enhance autonomous robots' navigation by integrating uncertainty-aware traversability maps for safer path planning in complex outdoor terrains.
- •For autonomous vehicle developers: Provide uncertainty estimates to help self-driving vehicles better interpret ambiguous environmental features in off-road or mixed terrain scenarios.
Tested on one dataset.
Authors
Ramona Häuselmann, Mario A. V. Saucedo, Christoforos Kanellakis, George Nikolakopoulos
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.