为自动驾驶视觉定位提供上下文感知的不确定性估计
Semantic and Feature Guided Uncertainty Quantification of Visual Localization for Autonomous Vehicles
- 结合图像特征与语义信息，构建轻量级误差模型预测定位不确定性
- 在恶劣天气下误差非高斯分布，混合高斯模型预测更准确
- 适合需要安全验证的自动驾驶系统，尤其关注复杂环境下的定位可靠性
传感器测量不确定性与深度学习网络的结合对机器人系统至关重要，尤其在自动驾驶等安全关键场景中。本文提出一种面向自动驾驶视觉定位的不确定性量化方法，基于图像选择位置。核心是使用轻量级传感器误差模型，将图像特征和语义信息映射到二维误差分布。该方法可基于匹配图像对的具体上下文进行不确定性估计，隐式捕捉未标注的关键因素（如城市/高速、动态/静态场景、冬夏季节）。在包含光照与天气变化（晴天、夜间、雪天）的Ithaca365数据集上验证了框架的准确性。评估了传感器+网络的不确定性量化，并结合独特的传感器门控方法进行贝叶斯定位滤波。结果表明，在恶劣天气和光照条件下，测量误差不符合高斯分布，采用混合高斯模型可更优预测。
原文摘要 · Abstract (English)
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.
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