Abstract
We investigate the global alignment of some 3-D spatial points, knowing the motion between pairwise nearby positions. A common application is to determine the trajectory of a mobile vision-based system from the scene images acquired along its track. Exploiting redundant measurements and the rigid body transformation P j = R (i,j) + t (i,j) as the observation model, we apply the mixed-model least squares estimation paradigm to develop recursive estimation algorithms under various scenarios. Results of experiments are given to demonstrate the performance for different noise levels in the observations and the improvements in the position estimation.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings - IEEE International Conference on Robotics and Automation |
| Pages | 5123-5128 |
| Number of pages | 6 |
| Volume | 2004 |
| Edition | 5 |
| State | Published - 2004 |
| Event | Proceedings- 2004 IEEE International Conference on Robotics and Automation - New Orleans, LA, United States Duration: Apr 26 2004 → May 1 2004 |
Other
| Other | Proceedings- 2004 IEEE International Conference on Robotics and Automation |
|---|---|
| Country | United States |
| City | New Orleans, LA |
| Period | 4/26/04 → 5/1/04 |
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ASJC Scopus subject areas
- Software
- Control and Systems Engineering
Cite this
Global alignment of sensor positions with noisy motion measurements. / Madjidi, Hossein; Negahdaripour, Shahriar.
Proceedings - IEEE International Conference on Robotics and Automation. Vol. 2004 5. ed. 2004. p. 5123-5128.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Global alignment of sensor positions with noisy motion measurements
AU - Madjidi, Hossein
AU - Negahdaripour, Shahriar
PY - 2004
Y1 - 2004
N2 - We investigate the global alignment of some 3-D spatial points, knowing the motion between pairwise nearby positions. A common application is to determine the trajectory of a mobile vision-based system from the scene images acquired along its track. Exploiting redundant measurements and the rigid body transformation P j = R (i,j) + t (i,j) as the observation model, we apply the mixed-model least squares estimation paradigm to develop recursive estimation algorithms under various scenarios. Results of experiments are given to demonstrate the performance for different noise levels in the observations and the improvements in the position estimation.
AB - We investigate the global alignment of some 3-D spatial points, knowing the motion between pairwise nearby positions. A common application is to determine the trajectory of a mobile vision-based system from the scene images acquired along its track. Exploiting redundant measurements and the rigid body transformation P j = R (i,j) + t (i,j) as the observation model, we apply the mixed-model least squares estimation paradigm to develop recursive estimation algorithms under various scenarios. Results of experiments are given to demonstrate the performance for different noise levels in the observations and the improvements in the position estimation.
UR - http://www.scopus.com/inward/record.url?scp=3042597005&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=3042597005&partnerID=8YFLogxK
M3 - Conference contribution
VL - 2004
SP - 5123
EP - 5128
BT - Proceedings - IEEE International Conference on Robotics and Automation
ER -