Aiming at the problem of anomalous and non-independent distribution of the image errors in the feature-based visual pose estimation, a method of monocular visual pose estimation based on the uncertainty of noise error established by projection vector is proposed. First, by using the covariance matrix to describe the uncertainty of the feature point direction and integrating the uncertainty of the feature point direction into the pose estimation, characteristic point measurement error with different degrees of directional uncertainty can be adapted that can makes the algorithm robust. Then, by introducing the projection vector and combining the depth information of each feature point to represent the collinearity error, the model nonlinear problem caused by the camera perspective projection can be eliminated that can make the algorithm have global convergence. Finally, we use global convergence theorem to prove the global convergence of the proposed algorithm. The results show that the proposed method has good robustness and convergence while adapting to different degrees of error uncertainty, which can meet practical engineering applications.

No abstract

We present UWSPSM, an algorithm of uncertainty weighted stereopsis pose solution method based on the projection vector which to solve the problem of pose estimation for stereo vision measurement system based on feature points. Firstly, we use a covariance matrix to represent the direction uncertainty of feature points, and utilize projection matrix to integrate the direction uncertainty of feature points into stereo-vision pose estimation. Then, the optimal translation vector is solved based on the projection vector of feature points, as well the depth is updated by the projection vector of feature points. In the absolute azimuth solution stage, the singular value decomposition algorithm is used to calculate the relative attitude matrix, and the above two stages are iteratively performed until the result converges. Finally, the convergence of the proposed algorithm is proved, from the theoretical point of view, by the global convergence theorem. Expanded into stereo-vision, the fixed relationship constraint between cameras is introduced into the stereoscopic pose estimation, so that only one pose parameter of the two images captured is optimized in the iterative process, and the two cameras are better bound as a camera, it can improve accuracy and efficiency while enhancing measurement reliability. The experimental results show that the proposed pose estimation algorithm can converge quickly, has high-precision and good robustness, and can tolerate different degrees of error uncertainty. So, it has useful practical application prospects.

It is time consuming to numerically solve fractional differential equations. The fractional ordinary differential equations may produce Toeplitz-plus-band triangular systems. An efficient iteration method for Toeplitz-plus-band triangular systems is presented withOMlogMcomputational complexity andOMmemory complexity in this paper, compared with the regular solution withOM2computational complexity andOM2memory complexity.Mis the discrete grid points. Some methods such as matrix splitting, FFT, compress memory storage and adjustable matrix bandwidth are used in the presented solution. The experimental results show that the presented method compares well with the exact solution and is 4.25 times faster than the regular solution.

No abstract