I'm prototyping a system that finds the 3D pose of a object in a video sequence. For this I minimize a error function involving the rotation and translation of the object as parameters and two sets of points (polluted with gaussian noise but no outliers) as data.I have tried to parameterize the rotation matrix both using 3 euler angles, and a rotation vector. Then I test my system with a synthetic data sequence and I see I obtain substantially less error with the 3 euler angles parameterization that with the rotation vector. I was expecting the opposite (notice the sequence I use doesn't result in any gimbal locks for the euler angles).  This makes me wonder if I'm using the rotation vector parameterization correctly. The steps I follow are, first I convert the rotation matrix to rotation vector (using the Rodrigues formula available in OpenCV) the resulting 3-vector is part of the parameters of my error function. Inside the error function I convert the rotation vector to rotation matrix and compute the residuals. I'm using python and scipy.optimize.leastsq (which internally uses MINPACK.lmdif) to minimize this error function. leastsq  will compute the jacobian needed for the minimization numerically from the error function. After reading a bit of theory about the lie group SO(3) and the lie algebra so(3), I understand that the so(3) (for me the rotation vector) corresponds to a tangent plane at the identity of SO(3). So my rotation vector is a local approximation to the corresponding rotation matrix.What I thought that maybe I'm missing in my minimization is to convert the rotation vector back to a rotation matrix after each minimization step, and then back to a rotation vector (to take a new local approximation to the rotation matrix) for the next step. I can't test this possibility as I'm using a standard minimizer, which doesn't allow to do this.  Now I think that the Jacobian calculated at each minimization step should be enough to direct the rotation vector parameters in the correct direction without the conversions I mention in the above paragraph. However, I'm not fully sure and as I get worse performance with the rotation vector parameterization wrt. the euler angle parameterization, I assume I'm doing something wrong. Would someone clarify/confirm the correct way of using the rotation vector in a minimization problem? 
Optimizing an error function involving rotation vectors in Python
optimization;python