Journal
IEEE ROBOTICS AND AUTOMATION LETTERS
Volume 6, Issue 3, Pages 4758-4765Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LRA.2021.3068694
Keywords
State estimation; Random variables; Covariance matrices; Task analysis; Noise measurement; Kalman filters; Estimation error; Sensor fusion; distributed robot systems
Categories
Funding
- FRQNT [2018-PR-253646]
- William Dawson Scholar Program
- NSERC
- CFI JELF Program
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This paper introduces a novel cascaded and decentralized filtering approach that approximates cross-covariances when local estimators consider distinct state vectors. The proposed estimator is validated in simulations and experiments on a three-dimensional attitude and position estimation problem, showing superior performance compared to other filtering approaches.
It is often convenient to separate a state estimation task into smaller local tasks, where each local estimator estimates a subset of the overall system state. However, neglecting cross-covariance terms between state estimates can result in overconfident estimates, which can ultimately degrade the accuracy of the estimator. Common cascaded filtering techniques focus on the problem of modelling cross-covariances when the local estimators share a common state vector. This letter introduces a novel cascaded and decentralized filtering approach that approximates the cross-covariances when the local estimators consider distinct state vectors. The proposed estimator is validated in simulations and in experiments on a three-dimensional attitude and position estimation problem. The proposed approach is compared to a naive cascaded filtering approach that neglects cross-covariance terms, a sigma point-based Covariance Intersection filter, and a full-state filter. In both simulations and experiments, the proposed filter outperforms the naive and the Covariance Intersection filters, while performing comparatively to the full-state filter.
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