Rigid body attitude estimation based on the Lagrange–d’Alembert principle

Izadi, Maziar; Sanyal, Amit K.

doi:10.1016/j.automatica.2014.08.010

Cited by 74 publications

(103 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Because of these properties of the function Φ, the critical points and their indices coincide for U 0 r and Φ(U 0 r ) [11]. Define the kinetic energy-like function:…”

Section: A Lagrangian From Measurement Residualsmentioning

confidence: 99%

“…where W is positive definite (not necessarily diagonal) which can be selected according to Lemma 3.2 in [11], and Φ :…”

Section: A Lagrangian From Measurement Residualsmentioning

confidence: 99%

“…Therefore, the total potential function is defined as the sum of the generalization of (16) defined in [11], [26] for attitude determination on SO(3), and the translational energy (17) as…”

Section: A Lagrangian From Measurement Residualsmentioning

confidence: 99%

“…From such optical (camera) measurements, the relative velocities (translational and angular) are also estimated. The variational attitude estimator recently appeared [10], [11], where it was shown to be almost globally asymptotically stable. The advantages of this scheme over Kalman-based schemes are reported in [9].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

GPS-denied relative motion estimation for fixed-wing UAV using the variational pose estimator

Izadi

Sanyal

Beard

et al. 2015

2015 54th IEEE Conference on Decision and Control (CDC)

Self Cite

View full text Add to dashboard Cite

Abstract-Relative pose estimation between fixed-wing unmanned aerial vehicles (UAVs) is treated using a stable and robust estimation scheme. The motivating application of this scheme is that of "handoff" of an object being tracked from one fixed-wing UAV to another in a team of UAVs, using onboard sensors in a GPS-denied environment. This estimation scheme uses optical measurements from cameras onboard a vehicle, to estimate both the relative pose and relative velocities of another vehicle or target object. It is obtained by applying the Lagrange-d'Alembert principle to a Lagrangian constructed from measurement residuals using only the optical measurements. This nonlinear pose estimation scheme is discretized for computer implementation using the discrete Lagranged'Alembert principle, with a discrete-time linear filter for obtaining relative velocity estimates from optical measurements. Computer simulations depict the stability and robustness of this estimator to noisy measurements and uncertainties in initial relative pose and velocities.

show abstract

“…Because of these properties of the function Φ, the critical points and their indices coincide for U 0 r and Φ(U 0 r ) [11]. Define the kinetic energy-like function:…”

Section: A Lagrangian From Measurement Residualsmentioning

confidence: 99%

“…where W is positive definite (not necessarily diagonal) which can be selected according to Lemma 3.2 in [11], and Φ :…”

Section: A Lagrangian From Measurement Residualsmentioning

confidence: 99%

“…Therefore, the total potential function is defined as the sum of the generalization of (16) defined in [11], [26] for attitude determination on SO(3), and the translational energy (17) as…”

Section: A Lagrangian From Measurement Residualsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

GPS-denied relative motion estimation for fixed-wing UAV using the variational pose estimator

Izadi

Sanyal

Beard

et al. 2015

2015 54th IEEE Conference on Decision and Control (CDC)

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, due to the nonlinear nature of the navigation equations, and in particular to the fact the orientation of the aircraft (i.e., the attitude) does not live in a vector space, the EKF may have some shortcomings. This has motivated the development of alternative filters, especially for attitude estimation, see e.g., [12], [19], [6], [15], [13], [10], [16], [20], [7].…”

Section: Introductionmentioning

confidence: 99%

Kalman filtering with a class of geometric state equality constraints

Chauchat

Barrau

Bonnabel

2017

2017 IEEE 56th Annual Conference on Decision and Control (CDC)

View full text Add to dashboard Cite

Finite time stable attitude estimation of rigid bodies with unknown dynamics

Warier

Sanyal

Viswanathan³

2019

Asian Journal of Control

Self Cite

View full text Add to dashboard Cite

An almost global finite time stable attitude estimation scheme for a rigid body that does not require the knowledge of the dynamics or the probability distribution of the sensor noise is presented. The attitude of the rigid body is estimated from measurements of at least two linearly independent known vectors and the angular velocity in the body-fixed frame. The estimation scheme is shown to be almost globally finite time stable using a Lyapunov analysis in the absence of measurement errors. A generalized Wahba's cost function, designed to be a Morse function on SO (3), is used to derive the nonlinear estimation scheme and show its stability. The proposed scheme is discretized as a geometric variational integrator for digital implementation. The stability and convergence of the estimation scheme are shown analytically, and validated through numerical simulations.

show abstract

Rigid body attitude estimation based on the Lagrange–d’Alembert principle

Cited by 74 publications

References 9 publications

GPS-denied relative motion estimation for fixed-wing UAV using the variational pose estimator

GPS-denied relative motion estimation for fixed-wing UAV using the variational pose estimator

Kalman filtering with a class of geometric state equality constraints

Finite time stable attitude estimation of rigid bodies with unknown dynamics

Contact Info

Product

Resources

About