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arXiv · 2303.06507

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

David J. Yoon·Timothy D. Barfoot
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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 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.

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David J. Yoon, Timothy D. Barfoot. 2023-03-11. Towards Consistent Batch State Estimation Using a Time-Correlated Measurement Noise Model. https://arxiv.org/abs/2303.06507

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