Abstract
Extended Kalman filters (EKF) have been proposed to estimate ego-motion and to recursively update scene structure in the form of 3-D positions of selected prominent features from motion and stereo sequences. Previous methods typically accommodate no more than a few dozen features for real-time processing. To maintain motion estimation accuracy, this calls for high contrast images to compute image feature locations with precision. Within manmade environments, various prominent corner points exist that can be extracted and tracked with required accuracy. However, prominent features are more difficult to localize precisely in natural scenes. Statistically, more feature points become necessary to maintain the same level of motion estimation accuracy and robustness. However, this imposes a computational burden beyond the capability of EKF-based techniques for real-time processing. A sequential dual EKF estimator utilizing stereo data is proposed for improved computation efficiency. Two important issues, unbiased estimation and stochastic stability are addressed. Furthermore, the dynamic feature set is handled in a more effective, efficient and robust way. Experimental results to demonstrate the merits of the new theoretical and algorithmic developments are presented.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - Third International Symposium on 3D Data Processing, Visualization, and Transmission, 3DPVT 2006 |
| Pages | 73-80 |
| Number of pages | 8 |
| DOIs | |
| State | Published - Dec 1 2007 |
| Event | 3rd International Symposium on 3D Data Processing, Visualization, and Transmission, 3DPVT 2006 - Chapel Hill, NC, United States Duration: Jun 14 2006 → Jun 16 2006 |
Other
| Other | 3rd International Symposium on 3D Data Processing, Visualization, and Transmission, 3DPVT 2006 |
|---|---|
| Country | United States |
| City | Chapel Hill, NC |
| Period | 6/14/06 → 6/16/06 |
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ASJC Scopus subject areas
- Computer Graphics and Computer-Aided Design
- Computer Networks and Communications
Cite this
EKF-based recursive dual estimation of structure & motion from stereo data. / Zhang, Hongshcng; Negahdaripour, Shahriar.
Proceedings - Third International Symposium on 3D Data Processing, Visualization, and Transmission, 3DPVT 2006. 2007. p. 73-80 4155712.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - EKF-based recursive dual estimation of structure & motion from stereo data
AU - Zhang, Hongshcng
AU - Negahdaripour, Shahriar
PY - 2007/12/1
Y1 - 2007/12/1
N2 - Extended Kalman filters (EKF) have been proposed to estimate ego-motion and to recursively update scene structure in the form of 3-D positions of selected prominent features from motion and stereo sequences. Previous methods typically accommodate no more than a few dozen features for real-time processing. To maintain motion estimation accuracy, this calls for high contrast images to compute image feature locations with precision. Within manmade environments, various prominent corner points exist that can be extracted and tracked with required accuracy. However, prominent features are more difficult to localize precisely in natural scenes. Statistically, more feature points become necessary to maintain the same level of motion estimation accuracy and robustness. However, this imposes a computational burden beyond the capability of EKF-based techniques for real-time processing. A sequential dual EKF estimator utilizing stereo data is proposed for improved computation efficiency. Two important issues, unbiased estimation and stochastic stability are addressed. Furthermore, the dynamic feature set is handled in a more effective, efficient and robust way. Experimental results to demonstrate the merits of the new theoretical and algorithmic developments are presented.
AB - Extended Kalman filters (EKF) have been proposed to estimate ego-motion and to recursively update scene structure in the form of 3-D positions of selected prominent features from motion and stereo sequences. Previous methods typically accommodate no more than a few dozen features for real-time processing. To maintain motion estimation accuracy, this calls for high contrast images to compute image feature locations with precision. Within manmade environments, various prominent corner points exist that can be extracted and tracked with required accuracy. However, prominent features are more difficult to localize precisely in natural scenes. Statistically, more feature points become necessary to maintain the same level of motion estimation accuracy and robustness. However, this imposes a computational burden beyond the capability of EKF-based techniques for real-time processing. A sequential dual EKF estimator utilizing stereo data is proposed for improved computation efficiency. Two important issues, unbiased estimation and stochastic stability are addressed. Furthermore, the dynamic feature set is handled in a more effective, efficient and robust way. Experimental results to demonstrate the merits of the new theoretical and algorithmic developments are presented.
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UR - http://www.scopus.com/inward/citedby.url?scp=47249137861&partnerID=8YFLogxK
U2 - 10.1109/3DPVT.2006.55
DO - 10.1109/3DPVT.2006.55
M3 - Conference contribution
AN - SCOPUS:47249137861
SN - 0769528252
SN - 9780769528250
SP - 73
EP - 80
BT - Proceedings - Third International Symposium on 3D Data Processing, Visualization, and Transmission, 3DPVT 2006
ER -