Vision-Aided Relative State Estimation for Approach and Landing on a Moving Platform with Inertial Measurements

2025-12-22

AI Summary

This work addresses the problem of estimating the relative pose and velocity of an unmanned aerial vehicle (UAV) with respect to an arbitrarily moving planar platform in 3D space, enabling precise approach and landing. The proposed method introduces a tightly coupled estimator that fuses measurements from two inertial measurement units (IMUs) and monocular vision—specifically, the platform’s center-line-of-sight direction and surface normal vector. A novel architecture combines an SO(3) Lie-group complementary filter with a linear Riccati-based cascaded observer; critically, it recovers unobservable attitude angles using only the platform’s linear acceleration under known normal-axis rotational constraints—a first in the literature. Theoretical analysis establishes local exponential convergence and almost-global asymptotic stability of the estimation error. Extensive simulations demonstrate high accuracy and strong robustness against dynamic disturbances, sensor noise, and large initial state errors.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Multi-modal Vision
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Security and Privacy: Large-scale security measurements
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

This paper tackles the problem of estimating the relative position, orientation, and velocity between a UAV and a planar platform undergoing arbitrary 3D motion during approach and landing. The estimation relies on measurements from Inertial Measurement Units (IMUs) mounted on both systems, assuming there is a suitable communication channel to exchange data, together with visual information provided by an onboard monocular camera, from which the bearing (line-of-sight direction) to the platform's center and the normal vector of its planar surface are extracted. We propose a cascade observer with a complementary filter on SO(3) to reconstruct the relative attitude, followed by a linear Riccati observer for relative position and velocity estimation. Convergence of both observers is established under persistently exciting conditions, and the cascade is shown to be almost globally asymptotically and locally exponentially stable. We further extend the design to the case where the platform's rotation is restricted to its normal axis and show that its measured linear acceleration can be exploited to recover the remaining unobservable rotation angle. A sufficient condition to ensure local exponential convergence in this setting is provided. The performance of the proposed observers is validated through extensive simulations.

Problem

• Estimating UAV-platform relative state during landing
• Using IMU and monocular vision for 3D motion tracking
• Designing stable cascade observers for position and attitude

Innovation

• Cascade observer with complementary filter on SO(3)
• Linear Riccati observer for relative position and velocity
• Exploits IMU data and monocular vision for estimation