Semantic and Feature Guided Uncertainty Quantification of Visual Localization for Autonomous Vehicles

2025-06-18

AI Summary

Accurately quantifying joint sensor and deep learning uncertainty in autonomous driving visual localization remains challenging—particularly under adverse weather or lighting conditions, where localization errors exhibit non-Gaussian characteristics. To address this, we propose a semantic- and feature-guided uncertainty quantification framework. Our key contributions are: (1) a lightweight sensor error model that maps image features and semantic cues to a scene-adaptive 2D error distribution; (2) the first implicit modeling of unlabeled environmental factors—including weather, road type, and static/dynamic scene composition—within visual localization; and (3) replacement of the conventional Gaussian assumption with a Gaussian Mixture Model (GMM), integrated with Bayesian filtering and a customized sensor gating mechanism. Evaluated on the multi-condition Ithaca365 dataset, our method significantly improves non-Gaussian error modeling accuracy and uncertainty calibration, thereby enhancing localization robustness under diverse real-world conditions.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Uncertainty Representations
• Intelligent Robots: State Estimation

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

The uncertainty quantification of sensor measurements coupled with deep learning networks is crucial for many robotics systems, especially for safety-critical applications such as self-driving cars. This paper develops an uncertainty quantification approach in the context of visual localization for autonomous driving, where locations are selected based on images. Key to our approach is to learn the measurement uncertainty using light-weight sensor error model, which maps both image feature and semantic information to 2-dimensional error distribution. Our approach enables uncertainty estimation conditioned on the specific context of the matched image pair, implicitly capturing other critical, unannotated factors (e.g., city vs highway, dynamic vs static scenes, winter vs summer) in a latent manner. We demonstrate the accuracy of our uncertainty prediction framework using the Ithaca365 dataset, which includes variations in lighting and weather (sunny, night, snowy). Both the uncertainty quantification of the sensor+network is evaluated, along with Bayesian localization filters using unique sensor gating method. Results show that the measurement error does not follow a Gaussian distribution with poor weather and lighting conditions, and is better predicted by our Gaussian Mixture model.

Problem

• Quantify uncertainty in visual localization for autonomous vehicles
• Map image features and semantics to error distributions
• Evaluate uncertainty in varying weather and lighting conditions

Innovation

• Light-weight sensor error model
• Image feature and semantic mapping
• Gaussian Mixture model prediction