Frontiers in Robotics and AI (Mar 2021)

MaskUKF: An Instance Segmentation Aided Unscented Kalman Filter for 6D Object Pose and Velocity Tracking

  • Nicola A. Piga,
  • Nicola A. Piga,
  • Fabrizio Bottarel,
  • Fabrizio Bottarel,
  • Claudio Fantacci,
  • Giulia Vezzani,
  • Ugo Pattacini,
  • Lorenzo Natale

DOI
https://doi.org/10.3389/frobt.2021.594583
Journal volume & issue
Vol. 8

Abstract

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Tracking the 6D pose and velocity of objects represents a fundamental requirement for modern robotics manipulation tasks. This paper proposes a 6D object pose tracking algorithm, called MaskUKF, that combines deep object segmentation networks and depth information with a serial Unscented Kalman Filter to track the pose and the velocity of an object in real-time. MaskUKF achieves and in most cases surpasses state-of-the-art performance on the YCB-Video pose estimation benchmark without the need for expensive ground truth pose annotations at training time. Closed loop control experiments on the iCub humanoid platform in simulation show that joint pose and velocity tracking helps achieving higher precision and reliability than with one-shot deep pose estimation networks. A video of the experiments is available as Supplementary Material.

Keywords