A recent line of work has shown that end-to-end optimization of Bayesian filters can be used to learn state estimators for systems whose underlying models are difficult to hand-design or tune, while retaining the core advantages of probabilistic state estimation. As an alternative approach for state estimation in these settings, we present an end-to-end approach for learning state estimators modeled as factor graph-based smoothers. By unrolling the optimizer we use for maximum a posteriori inference in these probabilistic graphical models, we can learn probabilistic system models in the full context of an overall state estimator, while also taking advantage of the distinct accuracy and runtime advantages that smoothers offer over recursive filters. We study this approach using two fundamental state estimation problems, object tracking and visual odometry, where we demonstrate a significant improvement over existing baselines. Our work comes with an extensive code release, which includes training and evaluation scripts, as well as Python libraries for Lie theory and factor graph optimization: https://sites.google.com/view/diffsmoothing/
翻译:最近的一项工作表明,可以使用巴伊西亚过滤器的端到端优化来学习基本模型难以手设计或调动的系统的国家测算器,同时保留概率国家估计的核心优势。作为在这些环境中进行国家估计的一种替代方法,我们提出了一个端到端方法,用于学习国家估计器作为以系数图为基础的光滑模型。通过释放我们用于在这些概率性图形模型中进行事后推断的最大优化的优化器,我们可以在总体状态估测器的整个背景下学习概率系统模型,同时利用滑动器为循环过滤器提供的独特准确性和运行时间优势。我们用两种基本状态估计问题,即物体跟踪和视觉odology来研究这一方法,在那里,我们展示了对现有基线的重大改进。我们的工作将带来广泛的代码发布,其中包括培训和评价脚本,以及用于修饰理论和要素图形优化的Python图书馆:https://sitessites.gogle.com/view/diffmologing/iminging:https://sites/gogle.gogle.com/dsmoting/polymotingsometing