In this paper, we present an algorithm for learning time-correlated measurement covariances for application in batch state estimation. We parameterize the inverse measurement covariance matrix to be block-banded, which conveniently factorizes and results in a computationally efficient approach for correlating measurements across the entire trajectory. We train our covariance model through supervised learning using the groundtruth trajectory. In applications where the measurements are time-correlated, we demonstrate improved performance in both the mean posterior estimate and the covariance (i.e., improved estimator consistency). We use an experimental dataset collected using a mobile robot equipped with a laser rangefinder to demonstrate the improvement in performance. We also verify estimator consistency in a controlled simulation using a statistical test over several trials.
翻译:在本文中,我们提出了一个用于在批量状态估算中应用的时间相关测量共变值学习的算法。我们将反向测量共变矩阵参数参数化为块状带,从而方便地将整个轨迹的测量结果乘以计算效率,从而形成一种与整个轨迹相关的测量方法。我们通过使用地面真实轨迹进行有监督的学习来培训我们的共变模型。在与时间相关测量的应用程序中,我们显示了平均后部估计值和共变值(即测算的一致性提高)两方面的性能改善。我们用一个配备激光测距仪的移动机器人收集的实验数据集来显示性能的改进。我们还利用数项试验的统计测试来验证控制模拟中的估计值一致性。</s>