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

Abstract-- 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 verify that our proposed method results in a consistent In the research field of probabilistic robotics, we formulate batch estimator in a controlled simulation via a statistical test state estimation using probability theory in order to handle over several trials.