State Estimation in Visual Inertial Autonomous Helicopter Landing Using Optimisation on Manifold

TL;DR

This paper presents a manifold-based nonlinear optimization approach for precise state estimation during autonomous helicopter landing, fusing IMU and monocular camera data to improve accuracy and computational efficiency.

Contribution

It introduces formal landmark Jacobian expressions and adapts an equality constrained Gauss-Newton method for this specific visual-inertial landing problem.

Findings

The method achieves high accuracy in state estimation.

The approach is computationally feasible for real-time applications.

Numerical simulations confirm the effectiveness of the proposed solution.

Abstract

Autonomous helicopter landing is a challenging task that requires precise information about the aircraft states regarding the helicopters position, attitude, as well as position of the helipad. To this end, we propose a solution that fuses data from an Inertial Measurement Unit (IMU) and a monocular camera which is capable of detecting helipads position in the image plane. The algorithm utilises manifold based nonlinear optimisation over preintegrated IMU measurements and reprojection error in temporally uniformly distributed keyframes, exhibiting good performance in terms of accuracy and being computationally feasible. Our contributions of this paper are the formal address of the landmarks Jacobian expressions and the adaptation of equality constrained Gauss-Newton method to this specific problem. Numerical simulations on MATLAB/Simulink confirm the validity of given claims.