Continuous-time State & Dynamics Estimation using a Pseudo-Spectral Parameterization

We present a novel continuous time trajectory representation based on a Chebyshev polynomial basis, which when governed by known dynamics models, allows for full trajectory and robot dynamics estimation, particularly useful for high-performance robotics applications such as unmanned aerial vehicles. We show that we can gracefully incorporate model dynamics to our trajectory representation, within a factor-graph based framework, and leverage ideas from pseudo- spectral optimal control to parameterize the state and the control trajectories as interpolating polynomials. This allows us to perform efficient optimization at specifically chosen points derived from the theory, while recovering full trajectory estimates. Through simulated experiments we demonstrate the applicability of our representation for accurate flight dynamics estimation for multirotor aerial vehicles. The representation framework is general and can thus be applied to a multitude of high-performance applications beyond multirotor platforms.

Authors

• Varun Agrawal
• Frank Dellaert

Keywords

• Statistical analysis
• Robot vision systems
• Estimation
• Chebyshev approximation
• Unmanned aerial vehicles
• Trajectory
• Sensors
• Dynamic Estimation
• Continuous-time State
• Dynamic Model
• Optimal Control
• Trajectory Control
• Chebyshev Polynomials
• Robot Dynamics
• Factor Graph
• Objective Function
• Gaussian Noise
• Fast Fourier Transform
• Control Function
• Angular Velocity
• Dimensional Vector
• Time Derivative
• Nonlinear Programming
• Equality Constraints
• Polynomial Of Degree
• State Trajectories
• Collocation Method
• Intrinsic Parameters
• Unit Circle
• Inertia Tensor
• Difference Matrix
• Skew-symmetric
• Arbitrary Time
• Arbitrary Interval
• Monocular