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

TL;DR

This paper introduces a continuous-time trajectory estimation method using Chebyshev polynomials and pseudo-spectral techniques, enabling accurate state and dynamics estimation for high-performance robots like drones.

Contribution

It proposes a novel Chebyshev polynomial-based trajectory representation integrated with known dynamics models within a factor-graph framework, leveraging pseudo-spectral optimal control ideas.

Findings

Effective in simulated multirotor flight dynamics estimation

Allows efficient optimization at selected points

General framework applicable to various high-performance robotics

Abstract

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.