Multi-camera rigid body pose estimation using higher-order dynamic models

Alec E. Forsman; David A. Schug; Anton J. Haug

doi:10.1117/12.2015539

20 May 2013 Multi-camera rigid body pose estimation using higher-order dynamic models

Alec E. Forsman, David A. Schug, Anton J. Haug

Proceedings Volume 8744, Automatic Target Recognition XXIII; 874407 (2013) https://doi.org/10.1117/12.2015539
Event: SPIE Defense, Security, and Sensing, 2013, Baltimore, Maryland, United States

Abstract

We describe a Bayesian filtering process that estimates the pose (3-D position and orientation) of a moving rigid body using multiple cameras. The estimator also produces an arbitrary number of pose derivatives. We first discuss various ways to represent 3-D orientation. Unfortunately all 3-parameter representations have areas of instability. Higher dimensional representations are stable but require unwieldy constraints. Our combination of an axis-angle vector with a unit quaternion represents orientation minimally while remaining stable under realistic circumstances. Our dynamic model of rigid body motion can include an arbitrary number of derivatives, and we explicitly develop it up to the third order. Our observation model takes a predicted pose and produces the 2-D locations in each camera's image plane of the visible features on the body's surface. We provide noise terms for both the dynamic and observation models. We describe how our models are used in extended and unscented Kalman filters, and also in a particle filter. As a baseline we also describe a non-linear least squares method that uses just our observation model. We construct a synthetic testing scenario, and use root-mean-square error analysis to grade the relative performance of each model/filter combination. We derive the Cramer-Rao lower bound that gives the best achievable performance for our particular scenario. Our results show that adding derivatives to the state vector significantly improves the accuracy of pose estimates, and we also show that an unscented Kalman filter with a second order dynamic model is best suited to the task.

Citation Download Citation

Alec E. Forsman, David A. Schug, and Anton J. Haug "Multi-camera rigid body pose estimation using higher-order dynamic models", Proc. SPIE 8744, Automatic Target Recognition XXIII, 874407 (20 May 2013); https://doi.org/10.1117/12.2015539

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available