Rigid Body Pose Estimation based on the Lagrange-d'Alembert Principle

TL;DR

This paper introduces a novel pose estimation method for rigid bodies using the Lagrange-d'Alembert principle, achieving stable, asymptotic convergence without requiring a dynamics model, and applicable with various measurement configurations.

Contribution

It applies variational mechanics to develop a stable, model-free pose estimation scheme that works with optical and inertial measurements, including discretization for implementation.

Findings

Stable asymptotic convergence in noise-free conditions

Effective estimation with limited measurements

Numerical simulations confirm bounded error in noisy scenarios

Abstract

Stable estimation of rigid body pose and velocities from noisy measurements, without any knowledge of the dynamics model, is treated using the Lagrange-d'Alembert principle from variational mechanics. With body-fixed optical and inertial sensor measurements, a Lagrangian is obtained as the difference between a kinetic energy-like term that is quadratic in velocity estimation error and the sum of two artificial potential functions; one obtained from a generalization of Wahba's function for attitude estimation and another which is quadratic in the position estimate error. An additional dissipation term that is linear in the velocity estimation error is introduced, and the Lagrange-d'Alembert principle is applied to the Lagrangian with this dissipation. A Lyapunov analysis shows that the state estimation scheme so obtained provides stable asymptotic convergence of state estimates to actual states in the absence of measurement noise, with an almost global domain of attraction. This estimation scheme is discretized for computer implementation using discrete variational mechanics, as a first order Lie group variational integrator. The continuous and discrete pose estimation schemes require optical measurements of at least three inertially fixed landmarks or beacons in order to estimate instantaneous pose. The discrete estimation scheme can also estimate velocities from such optical measurements. Moreover, all states can be estimated during time periods when measurements of only two inertial vectors, the angular velocity vector, and one feature point position vector are available in body frame. In the presence of bounded measurement noise in the vector measurements, numerical simulations show that the estimated states converge to a bounded neighborhood of the actual states.