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In this paper, we present an algorithm for learning time-correlatedmeasurement covariances for application in batch state estimation. Weparameterize the inverse measurement covariance matrix to be block-banded,which conveniently factorizes and results in a computationally efficientapproach for correlating measurements across the entire trajectory. We trainour covariance model through supervised learning using the groundtruthtrajectory. In applications where the measurements are time-correlated, wedemonstrate improved performance in both the mean posterior estimate and thecovariance (i.e., improved estimator consistency). We use an experimentaldataset collected using a mobile robot equipped with a laser rangefinder todemonstrate the improvement in performance. We also verify estimatorconsistency in a controlled simulation using a statistical test over severaltrials.

Towards Consistent Batch State Estimation Using a Time-Correlated Measurement Noise Model