Invariant Observer Design for a Helicopter UAV Aided Inertial Navigation System

Barczyk, Martin; Lynch, Alan F.

doi:10.1109/tcst.2012.2195495

Cited by 67 publications

(68 citation statements)

References 27 publications

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“…for all (u, a, b) ∈ U × G × G. As early noticed in [21], when the state space consists of a Lie group element χ t and some additional vector z t ∈ R k , and the dynamics write…”

Section: A Extending the Invariant Ekf Methodologymentioning

confidence: 99%

Tracking the Frenet-Serret frame associated to a highly maneuvering target in 3D

Pilté

Bonnabel

Barbaresco

2017

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

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Abstract-In this paper we consider the problem of tracking a maneuvering aircraft. The dynamical model used is based on assumptions of nearly constant tangential velocity, curvature, and torsion, of the target trajectory. Using the Frenet-Serret frame, that is, an element of the special orthogonal group SO(3) we cast the tracking problem into a filtering on Lie groups framework. We then use an invariant extended Kalman filter to estimate the various quantities involved. The resulting filter is simple to implement, and shown to gracefully accomodate some realistic target trajectories.

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“…for all (u, a, b) ∈ U × G × G. As early noticed in [21], when the state space consists of a Lie group element χ t and some additional vector z t ∈ R k , and the dynamics write…”

Section: A Extending the Invariant Ekf Methodologymentioning

confidence: 99%

Tracking the Frenet-Serret frame associated to a highly maneuvering target in 3D

Pilté

Bonnabel

Barbaresco

2017

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

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“…A relevant approach that is closely related to invariant observer is the complementary filter [33], which is designed directly on the matrix representation of the special orthogonal group SO (3). The invariant observer method is applied to the design of low-cost AHRS in [34][35][36], and is further used in the GPS-aided inertial navigation system for outdoor MAV applications in [37][38][39]. In addition, examples of complementary filter-based visual/inertial state estimation of a helicopter MAV can be found in [40].…”

Section: Related Workmentioning

confidence: 99%

Invariant Observer-Based State Estimation for Micro-Aerial Vehicles in GPS-Denied Indoor Environments Using an RGB-D Camera and MEMS Inertial Sensors

Tang³

et al. 2015

Micromachines

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This paper presents a non-linear state observer-based integrated navigation scheme for estimating the attitude, position and velocity of micro aerial vehicles (MAV) operating in GPS-denied indoor environments, using the measurements from low-cost MEMS (micro electro-mechanical systems) inertial sensors and an RGB-D camera. A robust RGB-D visual odometry (VO) approach was developed to estimate the MAV's relative motion by extracting and matching features captured by the RGB-D camera from the environment. The state observer of the RGB-D visual-aided inertial navigation was then designed based on the invariant observer theory for systems possessing symmetries. The motion estimates from the RGB-D VO were fused with inertial and magnetic measurements from the onboard MEMS sensors via the state observer, providing the MAV with accurate estimates of its full six degree-of-freedom states. Implementations on a quadrotor MAV and indoor flight test results demonstrate that the resulting state observer is effective in estimating the MAV's states without relying on external navigation aids such as GPS. The properties of computational efficiency and simplicity in gain tuning make the proposed invariant observer-based navigation scheme appealing for actual MAV applications in indoor environments. OPEN ACCESSMicromachines 2015, 6 488

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“…A tutorial of the filter formulation is given in [15], for readers interested in application. The design methodology is presented in the coordinate free framework of differential geometry.…”

Section: A Iekf Design Preliminariesmentioning

confidence: 99%

Relative localization with symmetry preserving observers

Silva

Mann

Gosine

2014

2014 IEEE 27th Canadian Conference on Electrical and Computer Engineering (CCECE)

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Symmetry preserving observer design is a recently developed approach for deriving estimators which exploits the invariant properties of nonlinear systems. Multi-robot localization is inherently a system operating on SE(3), thereby posing an interesting problem for application of the filter. This paper presents an invariant extended Kalman filter design for the problem of multi-robot relative localization in 2.5D, for application in ground and aerial mobile platforms. A detailed derivation of the invariant filter is presented with numerical results analyzing its performance against a traditional EKF approach to the problem. The tracking errors, stability to random initialization and robustness to changing noise characteristics are evaluated. The strong geometric basis of the filter results in linear Kalman like gain convergence behavior which is desirable for numerical stability and applicability as a low cost scheduled gain observer to the problem.

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Invariant Observer Design for a Helicopter UAV Aided Inertial Navigation System

Cited by 67 publications

References 27 publications

Tracking the Frenet-Serret frame associated to a highly maneuvering target in 3D

Tracking the Frenet-Serret frame associated to a highly maneuvering target in 3D

Invariant Observer-Based State Estimation for Micro-Aerial Vehicles in GPS-Denied Indoor Environments Using an RGB-D Camera and MEMS Inertial Sensors

Relative localization with symmetry preserving observers

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