System and method for hybrid optical/inertial headtracking via numerically stable Kalman filter
First Claim
1. A hybrid headtracking system incorporating a numerically stable Kalman fitter, comprising:
- at least one platform-referenced inertial measurement unit (IMU) configured to be rigidly mounted to a mobile platform associated with a platform reference frame, the at least one platform-referenced IMU configured to provide platform-referenced position and orientation (pose) data;
at least one head-referenced IMU configured to be rigidly mounted to a head of a user, the head associated with a head reference frame, the at least one head-referenced IMU configured to provide head-referenced pose data;
at least one aiding device configured to be rigidly mounted to at least one of the mobile platform and the head and to provide first estimated pose data, the at least one aiding device including at least one aiding sensor configured to generate the first estimated pose data based on one or more fiducial markers;
anda controller configured to be coupled to the platform-referenced IMU, the head-referenced IMU, and the at least one aiding device, the controller including at least one processor configured to;
receive the head-referenced pose data, the platform-referenced pose data, and the first estimated pose data;
determine at least one of a first error model associated with the head-referenced pose data, a second error model associated with the platform-referenced pose data, and a third error model associated with the first estimated pose data;
generate second estimated pose data based on the head-referenced pose data and the platform-referenced pose data;
generate, via the numerically stable Kalman filter, time-propagated pose data based on the second estimated pose data and one or more of the first error model and the second error model;
andgenerate, via the numerically stable Kalman fitter, at least one of corrected pose data and a corrected error model based on the time-propagated pose data, the first estimated pose data, and the at least one third error model.
1 Assignment
0 Petitions
Accused Products
Abstract
A system and related method for hybrid headtracking receives head-referenced pose data from a head-mounted IMU and platform-referenced or georeferenced position and orientation (pose) data from a platform-mounted IMU, determines error models corresponding to uncertainties associated with both IMUs, and performs an initial estimate of head pose relative to the platform reference frame based on the head-referenced and platform-referenced pose data. A numerically stable UD factorization of the Kalman filter propagates the estimated head pose data forward in time and corrects the initial head pose estimate (and may correct the error models associated with the head-mounted and platform-mounted IMUs) based on secondary head pose estimates, and corresponding error models, received from an optical or magnetic aiding device. The corrected head pose data is forward to a head-worn display to ensure high accuracy of displayed imagery and symbology.
39 Citations
20 Claims
-
1. A hybrid headtracking system incorporating a numerically stable Kalman fitter, comprising:
-
at least one platform-referenced inertial measurement unit (IMU) configured to be rigidly mounted to a mobile platform associated with a platform reference frame, the at least one platform-referenced IMU configured to provide platform-referenced position and orientation (pose) data; at least one head-referenced IMU configured to be rigidly mounted to a head of a user, the head associated with a head reference frame, the at least one head-referenced IMU configured to provide head-referenced pose data; at least one aiding device configured to be rigidly mounted to at least one of the mobile platform and the head and to provide first estimated pose data, the at least one aiding device including at least one aiding sensor configured to generate the first estimated pose data based on one or more fiducial markers; and a controller configured to be coupled to the platform-referenced IMU, the head-referenced IMU, and the at least one aiding device, the controller including at least one processor configured to; receive the head-referenced pose data, the platform-referenced pose data, and the first estimated pose data; determine at least one of a first error model associated with the head-referenced pose data, a second error model associated with the platform-referenced pose data, and a third error model associated with the first estimated pose data; generate second estimated pose data based on the head-referenced pose data and the platform-referenced pose data; generate, via the numerically stable Kalman filter, time-propagated pose data based on the second estimated pose data and one or more of the first error model and the second error model; and generate, via the numerically stable Kalman fitter, at least one of corrected pose data and a corrected error model based on the time-propagated pose data, the first estimated pose data, and the at least one third error model. - View Dependent Claims (2, 3, 4, 5, 6, 7)
-
-
8. A head-worn display (HWD) incorporating a hybrid headtracking system, comprising:
-
a controller including at least one control processor configured to; receive platform-referenced position and orientation (pose) data from at least one platform-referenced IMU rigidly mounted to a mobile platform and associated with a platform reference frame; receive head-referenced pose data from at least one head-referenced IMU rigidly mounted to a head of a user, the head associated with a head reference frame; determine at least one first error model associated with the platform-referenced pose data; determine at least one second error model associated with the head-referenced pose data; generate first estimated pose data based on the head-referenced pose data and the platform-referenced pose data; receive second estimated pose data from at least one aiding device rigidly mounted to one or more of the head and the mobile platform, the aiding device including at least one aiding sensor configured to generate the second estimated pose data based on one or more fiducial markers; implement at least one numerically stable Kalman fitter configured to; generate time-propagated pose data based on the first estimated pose data and one or more of the first error model and the second error model; and generate at least one of corrected pose data and a corrected error model based on the time-propagated pose data, the second estimated pose data, and the at least one third error model; and at least one display unit configured to be mounted to the head of the user, the at least one display unit comprising; at least one display processor configured to receive the corrected pose data from the control processor; and at least one display surface configured to display one or more images to the user based on the received corrected pose data. - View Dependent Claims (9, 10, 11, 12, 13)
-
-
14. A method for hybrid headtracking via a numerically stable Kalman filter, the method comprising:
-
receiving, via a controller coupled to a head worn display (HWD), platform-referenced position and orientation (pose) data from at least one platform-referenced inertial measurement unit (IMU) rigidly mounted to a mobile platform associated with a platform reference frame; receiving, via the controller, head-referenced pose data from at least one head-referenced IMU rigidly mounted to a head of a user; determining, via the controller, at least one first error model associated with the platform-referenced pose data; determining, via the controller, at least one second error model associated with the head-referenced pose data; generating, via the controller, first estimated pose data based on the platform-referenced pose data and the head-referenced pose data, the first estimated pose data corresponding to the head and relative to the platform reference frame; generating, via a Kalman filter associated with the controller, time-propagated pose data based on the first estimated pose data, the first error model, and the second error model; receiving, via the controller, second estimated pose data from at least one aiding device coupled to the controller, the aiding device rigidly mounted to at least one of the head and the mobile platform; determining, via the controller, at least one third error model associated with the second estimated pose data; and generating, via the Kalman filter, at least one of corrected pose data and a corrected error model based on the time-propagated pose data, the second estimated pose data, and the third error model. - View Dependent Claims (15, 16, 17, 18, 19, 20)
-
Specification