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Estimating Semantic Ambiguity via Gaussian Context Distributions for VLM-Driven Traversability Analysis
Ramona Häuselmann, Mario A. V. Saucedo, Christoforos Kanellakis, George Nikolakopoulos
TL;DR
VLM-based traversability estimation must contend with conflicting semantic predictions and uncertainty about how to interpret them. This paper anchors open-vocabulary predictions on a traversability scale and models them as a Gaussian Context Distribution, producing dense traversability and uncertainty maps. On GOOSE, the reported uncertainty correlates with real-world ambiguity sources, while the method has a boundary when semantic similarity diverges from physical traversability.
Problem
Dense traversability estimation with VLMs is challenged by domain shifts, opaque model errors, and conflicting prompts whose scores can obscure semantic ambiguity.
Source: §1 · p. 1Method
Conceptual anchoring places VLM concepts on a traversability scale, and the Gaussian Context Distribution uses its mean and spread to estimate traversability and uncertainty.
Source: §4 · p. 3Results
On the GOOSE dataset, the uncertainty metric correlates with real-world ambiguity sources, including visual artifacts and terrain overlap.
Source: §6 · p. 6Takeaways & Limitations
The distribution’s spread provides an uncertainty measure for ambiguous regions, which the method uses to help robots recognize and avoid areas where misclassification is likely.
Source: §1 · p. 2Semantic similarity does not always correspond to traversability; cases such as bush versus grass can produce separated peaks and low-probability gaps, motivating complementary evaluation metrics when semantic and physical properties diverge.
Source: §5.5 · pp. 5–6
Abstract
from arXiv · showhide
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.
1. INTRODUCTION
VLMs offer semantic scene understanding, but dense traversability estimation remains difficult when model confidence is opaque or conflicting. The paper uses conceptual anchors and a distribution over traversability cues to represent both traversability and ambiguity.
1. INTRODUCTION
- Conflicting high-confidence prompts can conceal semantic ambiguity, while a single moderate peak may be more reliable than competing peaks.Choosing the highest score can ignore conflicts such as simultaneous “grass” and “water” predictions.Source: §1 · p. 1
- Mixing intra-prompt and inter-prompt confidence can obscure uncertainty and misclassify hazardous regions as safe.The distinction is between confidence relative to pixels sharing a prompt and confidence across possible prompts.Source: §1 · p. 1
- The overview shows per-prompt confidence maps fused with conceptual anchors to produce per-patch distributions and dense traversability and uncertainty maps.The maps are used to determine the degree of visual ambiguity in the environment.Source: Figure 1 · p. 2
- The method treats VLM outputs as a distribution over traversability cues, preserving multiple activation peaks instead of choosing one class.Its spread indicates ambiguity, allowing the robot to recognize and avoid regions where misclassification is likely.Source: §1 · pp. 1–2
2. RELATED WORK
Prior traversability work spans geometry, learned terrain assessment, sensor fusion, and VLM-based navigation. This paper presents an ambiguity-aware vision pipeline that grounds VLM predictions in physical traversability and evaluates it in real outdoor scenes.
2. RELATED WORK
- Related approaches include geometry and 3D mapping, learned traversability, LiDAR-IMU terrain classification, and VLM-based navigation.Examples include sensor-fusion terrain confidence for reactive navigation, RGB-depth prediction, and LiDAR voxel estimation.Source: §2 · p. 2
- Outdoor VLM navigation methods also use visual markings, proprioceptive terrain properties, or VLM-selected candidate trajectories.These approaches include CoNVOI, VLM-GroNav, and a CVAE-based trajectory generation and selection method.Source: §2 · p. 2
- The paper’s contribution is a vision-based pipeline that estimates contextual uncertainty and produces dense, fine-grained traversability and uncertainty maps.It evaluates the pipeline qualitatively and quantitatively against ground truth and state-of-the-art baselines in unstructured outdoor environments.Source: §2 · p. 2
3. PROBLEM STATEMENT
The task is to infer dense traversability and uncertainty maps from a single RGB image. Both outputs share the image’s spatial dimensions, with traversability representing ease and safety of passage and uncertainty representing ambiguity in that assessment.
3. PROBLEM STATEMENT
- Given a single RGB image, the objective is to estimate dense traversability and uncertainty maps.The mapping outputs T and U have the same spatial dimensions as the input image.Source: §3 · p. 2
- Traversability is a continuous score from 0 for completely untraversable terrain to 1 for optimally traversable terrain.Water and asphalt are given as examples of the two endpoints.Source: §3 · p. 3
- Uncertainty is a continuous value from 0 for low uncertainty to 1 for highly uncertain traversability estimates due to semantic ambiguity.Source: §3 · p. 3
4. METHODOLOGY
The method places descriptive terrain concepts on a normalized traversability scale, then combines VLM patch scores with those anchors. A Gaussian Context Distribution summarizes each patch, whose mean and standard deviation yield dense traversability and uncertainty maps.
4. METHODOLOGY
- Conceptual anchors place descriptive terrain concepts in ascending traversal safety and ease on a normalized [0, 1] scale.The method specifies a position for each concept, with water at 0 and asphalt at 1 as examples.Source: §4 · p. 3
- The VLM produces a semantic similarity heatmap for each concept, and each patch’s raw scores are normalized with softmax into concept weights.Each weight is associated with its concept’s anchor position.Source: §4 · p. 3
- Each patch’s weighted anchor positions define a Gaussian Context Distribution, formulated as a Gaussian mixture over the traversability scale.Each concept is a component centered at its anchor and weighted by the VLM confidence.Source: §4 · p. 3
- The distribution mean gives traversability, while its standard deviation measures context uncertainty; high spread indicates multimodality or conflicting concepts.Calculating these moments across patches produces dense traversability and uncertainty maps.Source: §4 · p. 3
5. EXPERIMENTAL EVALUATION
The evaluation uses CLIPSeg on the real-world GOOSE dataset, assessing context distributions, traversability scores, uncertainty, and semantic ambiguity. Across qualitative and baseline comparisons, uncertainty tracks ambiguity, while the proposed method has competitive performance and lower mean error with tighter error distributions in challenging scenarios.
5.1 Qualitative Evaluation
- Grass yields a tight, low-uncertainty distribution, wooden planks a broader one, and reflective puddle borders a dispersed, multimodal distribution.The three locations are marked green, purple, and cyan, respectively.Source: Figure 2 · p. 4, §5.1 · p. 4
5.2 Groundtruth Comparison
- On GOOSE labels, asphalt and cobble score T > 0.8 versus bush or hedge at T ≈ 0.5; gravel and soil are highly traversable but more uncertain than asphalt.Asphalt uncertainty also has a significant upper whisker, indicating sensitivity to visual anomalies.Source: §5.2 · p. 4
5.3 Baseline Comparison
- The proposed GMM has comparable performance to baselines overall, with lower mean error and tighter error distributions in challenging scenarios.Fig. 4 compares methods on the GOOSE validation split against label-derived ground truth.Source: Figure 4 · p. 5, §5.3 · p. 5
5.4 Semantic Ambiguity Analysis
- Fences, shadows, and puddle reflections coincide with high uncertainty, even where fence-obscured or shadowed terrain is classified as traversable.Source: §5.4 · p. 5, Figure 5 · p. 5
- Terrain overlap raises uncertainty on snow-covered gravel and vegetation-obscured road edges; fallen leaves lower traversability and raise uncertainty relative to clean asphalt.Source: §5.4 · p. 5, Figure 6 · p. 6
5.5 Discussion
- Embedding similarity may misalign with physical traversability, as with bush and grass; the authors suggest complementary metrics and higher-order uncertainty statistics.They also identify prompt-free traversability estimation and navigation-stack integration as future directions.Source: §5.5 · pp. 5–6
6. CONCLUSIONS
The pipeline grounds open-vocabulary VLM predictions on a continuous physical traversability scale and models them as a Gaussian Context Distribution to estimate traversability and pixel-wise uncertainty. On GOOSE, uncertainty correlated with real-world ambiguity sources, and the method showed competitive baseline performance while providing statistical uncertainty estimates.
6. CONCLUSIONS
- Conceptual Anchoring grounds open-vocabulary VLM predictions on a continuous physical traversability scale, while a Gaussian Context Distribution yields dense traversability and pixel-wise uncertainty maps.The evaluation used the GOOSE dataset.Source: §6 · p. 6
- On GOOSE, the uncertainty metric effectively correlated with real-world ambiguity sources, including reflections, terrain overlaps, and semantic novelty.Source: §6 · p. 6
- The method showed competitive performance against the baseline while providing statistically valid uncertainty estimates unavailable from traditional maximization-based approaches.Source: §6 · p. 6
- The framework is presented as a step toward safer navigation by distinguishing confirmed safe terrain from semantically ambiguous areas requiring caution.Source: §6 · p. 6