Minimization on the Lie Group SO(3) Refering to a question previously asked Jacobian matrix of the Rodrigues' formula (exponential map). It was suggested in one of the answer to only calculate simpler Jacobian $\left.\frac{\partial}{\partial \mathbf p}\exp(\hat{\mathbf p})\right|_{\mathbf p=\mathbf 0}$ with $\exp(\hat{\mathbf p})$ being identity.
Similar idea is suggested in a technical report on Minimization on the Lie Group SO(3) and Related Manifolds .
Using Gauss-Newton based strategy, it was suggested to solve for $\delta$:
$$ \mathtt J^\top\mathtt J \delta = -\mathtt J^\top f(\mathtt R^{(m)})$$
Final update rule:
$$ \mathtt R^{(m+1)}= \exp(\hat\delta)\cdot \mathtt R^{(m)}.$$
Since, Gauss-Newton stems from the Taylor series approximation. To optimize an objective function $\mathcal O(\mathtt R^{(m)})$:
$$\mathcal O(\mathtt R^{(m+1)}) \approx \mathcal O(\mathtt R^{(m)}) + \mathbb g^\top\delta + \delta^\top \mathtt H \delta$$
The gradient $\mathbb g$ and Hessian $\mathtt H$ are calculated at the point corresponding to $\mathtt R^{(m)}$ which is initially $\mathbf p = \mathbf 0$. 
My question: As soon as we update $\mathtt R^{(m)}$, it no longer corresponds to $\mathbf p = \mathbf 0$. So, we should calculate gradient and Hessian at different $\mathbf p$ i.e. $\mathbf p^{(m+1)} =  \delta + \mathbf p^{(m)}$. Can someone explain the idea behind only using the gradient and Hessian at $\mathbf p = \mathbf 0$? (This was suggested in the technical report above and the question referred.) 
 A: I am trying to solve the confusion, not using a rigorous
mathematical argument, but rather by illustrating the similarity of standard Gauss Newton over the Euclidean Space and  Gauss Newton wrt. to a matrix Lie Group.
Let us first look at the derivative of a scalar function $f$:
$$\frac{\partial f(x)}{\partial x} :=  \lim_{y \to x} \frac{f(y)-f(x)}{y-x}$$
Now with $$\lim_{y \to x} \frac{f(y)-f(x)}{y-x} \overset{y=p+x}{=} \lim_{p \to 0} \frac{f(p+x)-f(x)}{p} 
 =: \left.\frac{\partial f(p+x)}{\partial p}\right|_{p=0}$$
Thus, we get:
$$\frac{\partial f(x)}{\partial x} =  \left.\frac{\partial f(p+x)}{\partial p}\right|_{p=0}~.$$
In other words, any derivative $\frac{\partial f(x)}{\partial x}$  can be written as a derivative around zero  $\left.\frac{\partial f(p+x)}{\partial p}\right|_{p=0}$.
(Source: Derivative as derivative around zero?)
Using a similar argument we can show that it holds for partial derivative of a function defined over the Euclidean vector space:
$$\frac{\partial f(\mathbf x)}{\partial   x_i} =  \left.\frac{\partial f(\mathbf  p+ \mathbf  x)}{\partial  p_i}\right|_{\mathbf  p=0}~.$$
Hence, the standard Gauss Newton Scheme can be rewritten as:
$$ \mathbf J^\top\mathbf J \delta = -\mathbf J^\top f(\mathbf x) \quad\text{with}\quad \mathbf J :=\left.\frac{\partial f(\mathbf p+\mathbf x_m)}{\partial  \mathbf p} \right|_{\mathbf p =\mathbf 0}$$
With the update rule:
$$ \mathbf x_{m+1}= \mathbf \delta + \mathbf x_m.$$
Thus, we can conclude that even the standard Gauss Newton approach can be seen 
as an optimisation around the identity ($\mathbf p =\mathbf 0$).
Obviously, the Jacobian needs to be recalculated after each update!

Maybe this answers the question already but let me continue:
A matrix Lie group can be seen as a generalization over the Euclidean vector space.
Thus, the Euclidean vector space is a trivial example of a matrix Lie group. 
We represent a vector $\mathbf a \in \mathbb{R}^n$ as a $(n+1)\times (n+1)$ matrix
$$\mathbf{A} :=  \begin{bmatrix}\mathbf{I}_{n\times n} &\mathbf a\\\mathbf{O}_{1\times n}&1 \end{bmatrix} $$
 the vector addition is written as matrix multiplication 
$$\begin{bmatrix}\mathbf{I}_{n\times n} &\mathbf a\\\mathbf{O}_{1\times n}&1 \end{bmatrix}\cdot \begin{bmatrix}\mathbf{I}_{n\times n}&\mathbf b\\\mathbf{O}_{1\times n}&1 \end{bmatrix} = \begin{bmatrix}\mathbf{I}_{n\times n} &\mathbf a+\mathbf b\\\mathbf{O}_{1\times n}&1 \end{bmatrix}$$
and the matrix exponential is simply the identity:
$$ \exp(\hat{\mathbf a}) = \begin{bmatrix}\mathbf{I}_{n\times n} &\mathbf a\\\mathbf{O}_{1\times n}&1 \end{bmatrix}~.$$
Using these new conventions, we can rewrite the Gauss Newton scheme ($\mathbf A$ instead of $\mathbf a$, matrix multiplication instead of vector addition, 
$\exp(\hat{\mathbf p})$ instead of $\mathbf p$).
Finally we get the following Jacobian and update rule for the generalized Gauss Newton Scheme:
$$\mathbf J :=\left.\frac{\partial f(\exp(\hat{\mathbf p})\cdot\mathbf A^{(m)})}{\partial \mathbf p} \right|_{\mathbf p =\mathbf 0}$$
and
$$ \mathbf A^{(m+1)}= \exp(\hat\delta)\cdot \mathbf A^{(m)}.$$
A: I think the problem lies in "As soon as we update $\mathtt R^{(m)}$, it no longer corresponds to $\mathbf p = \mathbf 0$". As I understand the answer you're referring to, it does; $\mathbf p$ is just the change, not the whole thing. It says
$$
\mathtt J :=\left.\frac{\partial f(\exp(\hat{\mathbf p})\cdot\mathtt R^{(m)})}{\partial \mathbf p} \right|_{\mathbf p =\mathbf 0}\;,
$$
so $f$ is being evaluated at $\exp(\hat{\mathbf p})$ times the current rotation, and $\mathbf p = \mathbf 0$ does correspond to $\mathtt R^{(m)}$.
A: The paper refers to the parameterization as "local" around the point $R_0$. At every iteration of the minimization the reference point is updated by applying the rotation associated to the step $\omega$ given by the Newton update.
So essentially "small" increments are calculated in a changing reference frame always with respect to the last result.
The purpose of the local parameterization is to avoid pathologies (e.g. gimbal lock of Euler angles) in a global representation, that originate from the incompatibility of the rotations with $\mathbb{R}^3$.
