环境感知的平滑GNSS协方差动态学习
Environment-Aware Learning of Smooth GNSS Covariance Dynamics for Autonomous Racing
- Department of Computing and Mathematical Sciences, California Institute of Technology（计算与数学科学系，加州理工学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出LACE框架，通过动态建模GNSS协方差动态，提升自动驾驶赛车在GNSS退化环境中的定位性能和协方差估计平滑性。
AI中文摘要：
确保准确且稳定的状态估计是一项具有挑战性的任务，对于安全关键领域如高速自动驾驶赛车至关重要，其中测量不确定性必须既适应环境又在时间上平滑以用于控制。在本工作中，我们开发了一个基于学习的框架LACE，能够直接建模GNSS测量协方差的时序动态。我们将协方差演变建模为一个指数稳定的动态系统，其中深度神经网络（DNN）通过注意力机制学习从环境特征中预测系统的过程噪声。通过使用收缩稳定性并系统地施加谱约束，我们正式提供了所得到的协方差动态的指数稳定性和平滑性保证。我们在AV-24自动驾驶赛车上验证了我们的方法，证明在具有挑战性的GNSS退化环境中，改进了定位性能并获得了更平滑的协方差估计。我们的结果突显了动态建模感知不确定性在与控制敏感性紧密耦合的状态估计问题中的潜力。
英文摘要：
Ensuring accurate and stable state estimation is a challenging task crucial to safety-critical domains such as high-speed autonomous racing, where measurement uncertainty must be both adaptive to the environment and temporally smooth for control. In this work, we develop a learning-based framework, LACE, capable of directly modeling the temporal dynamics of GNSS measurement covariance. We model the covariance evolution as an exponentially stable dynamical system where a deep neural network (DNN) learns to predict the system's process noise from environmental features through an attention mechanism. By using contraction-based stability and systematically imposing spectral constraints, we formally provide guarantees of exponential stability and smoothness for the resulting covariance dynamics. We validate our approach on an AV-24 autonomous racecar, demonstrating improved localization performance and smoother covariance estimates in challenging, GNSS-degraded environments. Our results highlight the promise of dynamically modeling the perceived uncertainty in state estimation problems that are tightly coupled with control sensitivity.
