A semi-global model-based state observer for the quadrotor using only inertial measurements

Martin, Philippe; Sarras, Ioannis

doi:10.1109/cdc.2016.7799367

Cited by 8 publications

(5 citation statements)

References 11 publications

(18 reference statements)

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“…In this subsection we will consider the problem of localization of each agent with respect to its neighbors by incorporating local, noiseless measurements, and considering a static target. This will be achieved by means of a designed nonlinear observer based on the invariant-manifold observer methodology, see Astolfi et al 27 and Karagiannis and Astolfi 18 for the general setting and Martin and Sarras, 28 Martin and Sarras, 29 and Sarras et al 12 for recent applications on MAVs. Proposition 1.…”

Section: Single Vehicle Localization From Direct Local Measurements: mentioning

confidence: 99%

Collaborative multiple micro air vehicles’ localization and target tracking in GPS-denied environment from range–velocity measurements

Sarras

Marzat

Bertrand

et al. 2018

International Journal of Micro Air Vehicles

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We treat the problem of simultaneous collaborative multiple micro air vehicles' localization and target tracking using time-varying range and (relative and absolute) velocity measurements. The proposed solution combines robustly local nonlinear observers that estimate the relative positions between agents and their neighbors, and cooperative filters that fuse each agent's local estimates to globally localize them with respect to the target (and therefore to each other). These estimates are then introduced in a dynamic consensus-type control law that ensures the global collective target tracking while simultaneously estimating the target's velocity, without needing any external reference which makes it applicable in GPS-denied environments. Finally, a simulation scenario is studied in order to show the efficiency of the proposed solution.

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Section: Single Vehicle Localization From Direct Local Measurements: mentioning

confidence: 99%

Collaborative multiple micro air vehicles’ localization and target tracking in GPS-denied environment from range–velocity measurements

Sarras

Marzat

Bertrand

et al. 2018

International Journal of Micro Air Vehicles

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“…A dynamical model of the system can be used to predict the accelerations and compensate for them. This prediction can be based on the forces models, either in the case of unmanned aerial vehicles [14,15] or legged robots [4,17]. However this solution is specific to every dynamical system and requires to identify many dynamical parameters.…”

Section: Introductionmentioning

confidence: 99%

Velocity-aided IMU-based Attitude Estimation

Benallegue,

Cisneros

et al. 2020

Preprint

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This paper addresses the problem of estimating the attitude of a rigid body, which is subject to high accelerations and equipped with inertial measurement unit (IMU) and sensors providing the body velocity (expressed in the reference frame attached to the body). That issue can be treated differently depending on the level of confidence in the measurements of the magnetometer of the IMU, particularly with regard to the observation of the inclination component with respect to the vertical direction, rendering possible to describe the interaction with gravity. Two cases are then studied: either (i) the magnetometer is absent and only the inclination can be estimated, (ii) the magnetometer is present, giving redundancy and full attitude observability. In the latter case, the presented observer allows to tune how much the inclination estimation is influenced by the magnetometer. All state estimators are proposed with proof of almost global asymptotic stability and local exponential convergence. Finally, these estimators are compared with state-of-the-art solutions in clean and noisy simulations, allowing recommended solutions to be drawn for each case.

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“…Classical approaches for state estimation are typically based on filtering techniques such as extended Kalman filters (EKF), unscented Kalman filters or particle filters. However, nonlinear observers have increasingly become an alternative to these classical techniques, starting with the work of Salcudean on attitude observer [20] and subsequent contributions by other researchers [1], [5], [6], [8], [10], [15]- [19]. Full pose observer design has recently attracted some particular attention [2]- [4], [9], [11], [13]- [15], [21]- [23].…”

Section: Introductionmentioning

confidence: 99%

Riccati Observer Design for Pose, Linear Velocity and Gravity Direction Estimation Using Landmark Position and IMU Measurements

Hua

Allibert

2018

2018 IEEE Conference on Control Technology and Applications (CCTA)

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This paper revisits the problem of estimating the pose (i.e. position and attitude) of a robotic vehicle by combining landmark position measurements provided by a stereo camera with measurements of an Inertial Measurement Unit. The distinguished features with respect to similar works on the topic are two folds: First, the vehicle's linear velocity is not measured neither in the body frame nor in the inertial frame; Second, no prior knowledge on the gravity direction expressed in the inertial frame is required. Instead both the linear velocity and the gravity direction are estimated together with the pose. Another innovative feature of the paper relies on the idea of over-parametrizing the gravity direction vector evolving on the unit 2-sphere S 2 by an element of SO(3) so that the error system in first order approximations can be written in an "elegant" linear time-varying form. The proposed deterministic observer is accompanied with an observability analysis that points out an explicit observability condition under which local exponential stability is granted. Reported simulation results further indicate that the observer's domain of convergence is large.

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A semi-global model-based state observer for the quadrotor using only inertial measurements

Cited by 8 publications

References 11 publications

Collaborative multiple micro air vehicles’ localization and target tracking in GPS-denied environment from range–velocity measurements

Collaborative multiple micro air vehicles’ localization and target tracking in GPS-denied environment from range–velocity measurements

Velocity-aided IMU-based Attitude Estimation

Riccati Observer Design for Pose, Linear Velocity and Gravity Direction Estimation Using Landmark Position and IMU Measurements

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