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：本文提出了一种学习时间相关测量协方差的算法，用于批量状态估计。我们将逆测量协方差矩阵参数化为块带状矩阵，该矩阵便于分解，并形成一种计算高效的方法来关联整个轨迹上的测量值。我们通过使用真实轨迹的监督学习来训练协方差模型。在测量值时间相关的应用中，我们展示了平均后验估计和协方差（即改进的估计器一致性）性能的提升。我们利用配备激光测距仪的移动机器人收集的实验数据集来验证性能改进。同时，通过多轮试验的统计检验，在受控仿真环境中验证了估计器的一致性。