Estimating differential quantities from point cloud based on a linear fitting of normal vectors

Estimation of differential geometric properties on a discrete surface is a fundamental work in computer graphics and computer vision. In this paper, we present an accurate and robust method for estimating differential quantities from unorganized point cloud. The principal curvatures and principal directions at each point are computed with the help of partial derivatives of the unit normal vector at that point, where the normal derivatives are estimated by fitting a linear function to each component of the normal vectors in a neighborhood. This method takes into account the normal information of all neighboring points and computes curvatures directly from the variation of unit normal vectors, which improves the accuracy and robustness of curvature estimation on irregular sampled noisy data. The main advantage of our approach is that the estimation of curvatures at a point does not rely on the accuracy of the normal vector at that point, and the normal vectors can be refined in the process of curvature estimation. Compared with the state of the art methods for estimating curvatures and Darboux frames on both synthetic and real point clouds, the approach is shown to be more accurate and robust for noisy and unorganized point cloud data.

Robust Estimation of Parameters in Nonlinear Ordinary Differential Equation Models

Ordinary differential equation(ODE) models are widely used to model dynamic processes in many scientific fields.Parameter estimation is usually a challenging problem,especially in nonlinear ODE models.The most popular method,nonlinear least square estimation,is shown to be strongly sensitive to outliers.In this paper,robust estimation of parameters using M-estimators is proposed,and their asymptotic properties are obtained under some regular conditions.The authors also provide a method to adjust Huber parameter automatically according to the observations.Moreover,a method is presented to estimate the initial values of parameters and state variables.The efficiency and robustness are well balanced in Huber estimators,which is demonstrated via numerical simulations and chlorides data analysis.

Flatness-Based Control in Successive Loops for Unmanned Aerial Vehicles and Micro-Satellites

The control problem for the multivariable and nonlinear dynamics of unmanned aerial vehicles and micro-satellites is solved with the use of a flatness-based control approach which is implemented in successive loops.The state-space model of(i)unmanned aerial vehicles and(ii)micro-satellites is separated into two subsystems,which are connected between them in cascading loops.Each one of these subsystems can be viewed independently as a differentially flat system and control about it can be performed with inversion of its dynamics as in the case of input–output linearized flat systems.The state variables of the second subsystem become virtual control inputs for the first subsystem.In turn,exogenous control inputs are applied to the first subsystem.The whole control method is implemented in two successive loops and its global stability properties are also proven through Lyapunov stability analysis.The validity of the control method is confirmed in two case studies:(a)control and trajectories tracking for the autonomous octocopter,(ii)control of the attitude dynamics of micro-satellites.

A Nonlinear Optimal Control Approach for the Vertical Take-off and Landing Aircraft

The paper proposes a nonlinear optimal control approach for the model of the vertical take off and landing(VTOL)aircraft.This aerial drone receives as control input a directed thrust,as well as forces acting on its wing tips.The latter forces are not perpendicular to the body axis of the drone but are tilted by a small angle.The dynamic model of the VTOL undergoes ap-proximate linearization with the use of Taylor series expansion around a temporary operating point which is recomputed at each iteration of the control method.For the approximately linearized model,an H-infinity feedback controller is designed.The linearization procedure relies on the computation of the Jacobian matrices of the state-space model of the VTOL aircraft.The proposed control method stands for the solution of the optimal control problem for the nonlinear and multivariable dynamics of the aerial drone,under model uncertainties and external per-turbations.For the computation of the contollr's feedback gains,an algebraic Riccati equation is solved at each time-step of the control method.The new nonlinear optimal control approach achieves fast and accurate tracking for all state variables of the VTOL aircnaft,under moderate variations of the control inputs.The stability properties of the control scheme are proven through Lyapunov analysis.