Abstract
The introduction of the full paper reviews some papers in the open literature and then proposes a novel OI fusion algorithm, which we believe is better. Sections 1 and 2 explain our better algorithm. Section 1 briefs OI algorithm. The core of section 2 consists of: (1) we use as error function, which is given by eq.(10), the quadratic sum of collinearity errors of all the feature points in target space obtained from two cameras; (2) we deduce the four-step iterative procedure to get the minimum target space collinearity error as indicated in eq.(12); (3) we estimate the target position and attitude through unit quaternion of the target, thus avoiding fundamentally the troublesome rotation matrix error in traditional algorithms. Simulation results, presented in Figs.2 through 5 and Table1, and their analysis demonstrate preliminarily that our novel OI fusion algorithm is significantly better in precision, anti-noise ability and stability performances.
Original language | English |
---|---|
Pages (from-to) | 559-563 |
Number of pages | 5 |
Journal | Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University |
Volume | 29 |
Issue number | 4 |
State | Published - Aug 2011 |
Keywords
- Algorithms
- Computer vision
- Estimation
- Navigation
- Orthogonal iteration (OI)
- Position and attitude
- Unit quaternion