Filter-Based Online System-Parameter Estimation for Multicopter UAVs

Böhm, Christoph; Scheiber, Martin; Weiß, Stephan

doi:10.15607/rss.2021.xvii.087

Cited by 12 publications

(9 citation statements)

References 17 publications

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“…Finally, the proposed EqF was evaluated, and compared with a MEKF, on real-world in-flight data. Raw IMU readings were recorded at 200 Hz on a AscTec Hummingbird quadcopter flying a Lissajous curve [24], and raw pose measurements gathered at ∼30 Hz from a OptiTrack motion capture system. Velocity measurements were derived by numerical differentiation of the filtered position measurements.…”

Section: Methodsmentioning

confidence: 99%

Equivariant Filter Design for Inertial Navigation Systems with Input Measurement Biases

Fornasier¹,

Ng²,

Mahony³

et al. 2022

Preprint

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Inertial Navigation Systems (INS) are a key technology for autonomous vehicles applications. Recent advances in estimation and filter design for the INS problem have exploited geometry and symmetry to overcome limitations of the classical Extended Kalman Filter (EKF) approach that formed the mainstay of INS systems since the mid-twentieth century. The industry standard INS filter, the Multiplicative Extended Kalman Filter (MEKF), uses a geometric construction for attitude estimation coupled with classical Euclidean construction for position, velocity and bias estimation. The recent Invariant Extended Kalman Filter (IEKF) provides a geometric framework for the full navigation states, integrating attitude, position and velocity, but still uses the classical Euclidean construction to model the bias states. In this paper, we use the recently proposed Equivariant Filter (EqF) framework to derive a novel observer for biased inertial-based navigation in a fully geometric framework. The introduction of virtual velocity inputs with associated virtual bias leads to a full equivariant symmetry on the augmented system. The resulting filter performance is evaluated with both simulated and realworld data, and demonstrates increased robustness to a wide range of erroneous initial conditions, and improved accuracy when compared with the industry standard Multiplicative EKF (MEKF) approach.

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Section: Methodsmentioning

confidence: 99%

Equivariant Filter Design for Inertial Navigation Systems with Input Measurement Biases

Fornasier¹,

Ng²,

Mahony³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…III are implemented in Python. All system parameters are based on the quadrotor Hummingbird model of [12]. This previous work shows that it is possible to estimate the thrust force and drag moment coefficients, k f and k m .…”

Section: A Setup and Evaluation Methodsmentioning

confidence: 99%

“…All previously mentioned approaches do not consider model and state uncertainties to ensure robust trajectory tracking or accurate state/parameter estimation. 2) Estimation-aware Trajectories: State estimation with proper system modeling [12] relies on the design of sufficiently informative system input for accurate and fast estimate convergence. The works in [13]- [19] show that taking the estimation or parametric uncertainty into account drastically improves system parameter estimation results while allowing task execution.…”

Section: A Motivation and Related Workmentioning

confidence: 99%

“…3) Observability Metric: Online state estimation is one way of making the state x and parameters p available at run-time [12], [26], [27].…”

Section: A Objectives For Trajectory Optimizationmentioning

confidence: 99%

“…The estimator in [12] is the base for the derivation of the observability-aware trajectory optimization in our quadrotor UAV case study, Sec. IV.…”

Section: A Objectives For Trajectory Optimizationmentioning

confidence: 99%

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COP: Control & Observability-aware Planning

Böhm¹,

Brault²,

Delamare³

et al. 2022

Preprint

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In this research, we aim to answer the question: How to combine Closed-Loop State and Input Sensitivity-based with Observability-aware trajectory planning? These possibly opposite optimization objectives can be used to improve trajectory control tracking and, at the same time, estimation performance.Our proposed novel Control & Observability-aware Planning (COP) framework is the first that uses these possibly opposing objectives in a Single-Objective Optimization Problem (SOOP) based on the Augmented Weighted Tchebycheff method to perform the balancing of them and generation of Bézier curve-based trajectories. Statistically relevant simulations for a 3D quadrotor unmanned aerial vehicle (UAV) case study produce results that support our claims and show the negative correlation between both objectives. We were able to reduce the positional mean integral error norm as well as the estimation uncertainty with the same trajectory to comparable levels of the trajectories optimized with individual objectives.

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