Robust 3D Vision for Autonomous Robots

TL;DR

This paper introduces a fault-tolerant 3D vision system combining Kalman filtering and ICP algorithms for robust pose estimation of space objects, demonstrated through ground tests simulating autonomous rendezvous and docking.

Contribution

The paper presents a novel integrated Kalman filter and ICP approach with dynamic modeling for continuous, robust pose tracking of space objects in autonomous robotic systems.

Findings

Effective pose estimation during sensor signal loss

Enhanced ICP initialization accuracy through Kalman filter

Successful demonstration on satellite rendezvous simulation

Abstract

This paper presents a fault-tolerant 3D vision system for autonomous robotic operation. In particular, pose estimation of space objects is achieved using 3D vision data in an integrated Kalman filter (KF) and an Iterative Closest Point (ICP) algorithm in a closed-loop configuration. The initial guess for the internal ICP iteration is provided by the state estimate propagation of the Kalman filer. The Kalman filter is capable of not only estimating the target's states but also its inertial parameters. This allows the motion of the target to be predictable as soon as the filter converges. Consequently, the ICP can maintain pose tracking over a wider range of velocity due to the increased precision of ICP initialization. Furthermore, incorporation of the target's dynamics model in the estimation process allows the estimator continuously provide pose estimation even when the sensor temporally loses its signal namely due to obstruction. The capabilities of the pose estimation methodology is demonstrated by a ground testbed for Automated Rendezvous & Docking. In this experiment, Neptec's Laser Camera System (LCS) is used for real-time scanning of a satellite model attached to a manipulator arm, which is driven by a simulator according to orbital and attitude dynamics. The results showed that robust tracking of the free-floating tumbling satellite can be achieved only if the Kalman filter and ICP are in a closed-loop configuration.