SCAAT: incremental tracking with incomplete information

@article{Welch1997SCAATIT,
  title={SCAAT: incremental tracking with incomplete information},
  author={Greg Welch and Gary Bishop},
  journal={Proceedings of the 24th annual conference on Computer graphics and interactive techniques},
  year={1997}
}
  • G. Welch, G. Bishop
  • Published 3 August 1997
  • Computer Science
  • Proceedings of the 24th annual conference on Computer graphics and interactive techniques
The Kalman filter provides a powerful mathematical framework within which a minimum mean-square-error estimate of a user's position and orientation can be tracked using a sequence of single sensor observations, as opposed to groups of observations. We refer to this new approach as single-constraint-at-a-time or SCAAT tracking. The method improves accuracy by properly assimilating sequential observations, filtering sensor measurements, and by concurrently autocalibrating mechanical or electrical… 

Figures from this paper

Structure from Motion via Two-State Pipeline of Extended Kalman Filters
TLDR
A novel approach to on-line structure from motion is introduced, using a pipelined pair of extended Kalman filters to improve accuracy with a minimal increase in computational cost and a reduction of more than 50% in mean reprojection errors.
Seeker gyro calibration via model-based fusion of visual and inertial data
TLDR
This paper presents a motion model-based method for robust estimation of gyro error parameters via fusing the inertial measurements with the imaging sensor's data, and renders the estimator more robust to image noise and feature point extraction and tracking errors.
Real-time tracking for virtual environments using scaat kalman filtering and unsynchronised cameras
TLDR
This paper presents a real-time outside-in camera-based tracking system for wireless 3D pose tracking of a user’s head and hand in a virtual environment that provides less noisy pose estimates with a higher update rate than a conventional stereo triangulation approach.
HISTORY: The Use of the Kalman Filter for Human Motion Tracking in Virtual Reality
  • G. Welch
  • Engineering
    PRESENCE: Teleoperators and Virtual Environments
  • 2009
TLDR
The purpose of this paper is to acknowledge the approaching 50th anniversary of the Kalman filter with a look back at the use of the filter for human motion tracking in virtual reality (VR) and augmented reality (AR).
MARVIN: a Mobile Automatic Realtime Visual and INertial tracking system
TLDR
This thesis presents a hybrid Inertial/Optical tracking system for fullyenclosed projective displays that places minimal hardware on the user and no visible tracking equipment is placed within the immersive environment.
Motion estimation with incomplete information using omni-directional vision
  • Jong Weon Lee, U. Neumann
  • Computer Science
    Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101)
  • 2000
TLDR
A new motion estimation framework is presented and applied to omni-directional imagery using an implicit extended Kalman filter (IEKF) based on the rigidity and the depth independent constraints.
An Introduction to Kalman Filter
TLDR
A practical introduction to the discrete Kalman filter, a set of mathematical equations that provides an efficient computational means to estimate the state of a process, in a way that minimizes the mean of the squared error.
Kalman Filters for Time Delay of Arrival-Based Source Localization
TLDR
The proposed algorithm, although relying on an iterative optimization scheme, proved efficient enough for real-time operation and provides source localization accuracy superior to the standard spherical and linear intersection techniques.
Tracking Multiple Speakers with Probabilistic Data Association Filters
TLDR
A speaker tracking system based on an extended Kalman filter using time delays of arrival (TDOAs) as acoustic features is developed, which is generalized to a probabilistic data association filter (JPDAF), which maintains a separate state vector for each active speaker.
Kalman filter in computer vision
In statistics, the Kalman filter is a mathematical method named after Rudolf E. Kalman. Its purpose is to use measurements that are observed over time that contain noise (random variations) and other
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 110 REFERENCES
Multisensor data fusion for automated guided vehicles
The paper addresses multi-sensor data fusion for the navigation of a 4 wheel vehicle with two driven wheels. The main advantage of such a configuration is its flexibility concerning free motion and
Inertial head-tracker sensor fusion by a complementary separate-bias Kalman filter
  • E. Foxlin
  • Engineering
    Proceedings of the IEEE 1996 Virtual Reality Annual International Symposium
  • 1996
TLDR
The design of a Kalman filter is described to integrate the data from these two types of sensors in order to achieve the excellent dynamic response of an inertial system without drift, and without the acceleration sensitivity of inclinometers.
Position and Velocity Estimation Via Bearing Observations
The problem of performing target motion analysis using noisy bearing measurements derived from multiple observation platforms or from a single moving observer is addressed. For the latter case, the
Estimation of Object Motion Parameters from Noisy Images
TLDR
An approach is presented for the estimation of object motion parameters based on a sequence of noisy images that may be of use in situations where it is difficult to resolve large numbers of object match points, but relatively long sequences of images are available.
Autocalibration for virtual environments tracking hardware
TLDR
A back-projection system for adjusting the assumed locations of beacons in a head-mounted display tracking system is described; the calculated errors in the navigation system are used to compute adjustments to the beacon positions to reduce such errors.
On temporal-spatial realism in the virtual reality environment
TLDR
A predictive Kalman lter was designed to compensate for the delay in orientation data, and an anisotropic low pass low pass approach was devised to reduce the noise in position data.
Improving static and dynamic registration in an optical see-through HMD
TLDR
This paper offers improved registration in two areas: accurate static registration across a wide variety of viewing angles and positions and dynamic errors that occur when the user moves his head are reduced by predicting future head locations.
Multiple sensor fusion for navigation systems
TLDR
This paper describes how to combine and/or fuse vehicle sensors to improve the position accuracy from the DGPS, and proposes a theoretical background for sensor fusion theory.
Predictive tracking for augmented reality
TLDR
This dissertation demonstrates that predicting future head locations is an effective approach for significantly reducing dynamic errors and can also estimate the maximum possible time-domain error and the maximum tolerable system delay given a specified maximum time- domain error.
...
1
2
3
4
5
...