Uncertainty-Aware Gaussian Map for Vision-Language Navigation
Vision-Language Navigation (VLN) requires an agent to navigate 3D environments following natural language instructions. During navigation, existing agents commonly encounter perceptual uncertainty, such as insufficient evidence for reliable grounding or ambiguity in interpreting spatial cues, yet they typically ignore such information when predicting actions. In this work, we explicitly model three forms of perceptual uncertainty (i.e., geometric, semantic, and appearance uncertainty) and integrate them into the agent's observation space to enable informed decision-making. Concretely, our agent first constructs a Semantic Gaussian Map (SGM), composed of differentiable 3D Gaussian primitiv...
| Authors | Jianzhe Gao, Rui Liu, Yuxuan Xu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 26 May 2026 |
| Last updated at source | 26 May 2026 |
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