The Manifold Particle Filter for State Estimation on High-dimensional Implicit Manifolds

Michael C. Koval, Matthew Klingensmith, Siddhartha S. Srinivasa, Nancy Pollard and Michael Kaess
Conference Paper, IEEE Intl. Conf. on Robotics and Automation, ICRA, May, 2017

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We estimate the state of a noisy robot arm and underactuated hand using an implicit Manifold Particle Filter (MPF) informed by contact sensors. As the robot touches the world, its state space collapses to a contact manifold that we represent implicitly using a signed distance field. This allows us to extend the MPF to higher (six or more) dimensional state spaces. Earlier work, which explicitly represents the contact manifold, was only capable of scaling to three dimensions. Through a series of experiments, we show that the implicit MPF converges faster and is more accurate than a conventional particle filter during periods of persistent contact. We present three methods of drawing samples from an implicit contact manifold, and compare them in experiments.

author = {Michael C. Koval and Matthew Klingensmith and Siddhartha S. Srinivasa and Nancy Pollard and Michael Kaess},
title = {The Manifold Particle Filter for State Estimation on High-dimensional Implicit Manifolds},
booktitle = {IEEE Intl. Conf. on Robotics and Automation, ICRA},
year = {2017},
month = {May},
} 2018-02-07T15:03:03-04:00