Understanding 2D alignment using least squares In Computer Vision Algorithms and Applications book by Richard Szeliski, the author explains 2D alignment using least squares as follows:


  Given a set of matched feature points ${(x_i, x_i')}$ and a planar parametric transformation of the form
  $$x' = f(x;p),$$
  how can we produce the best estimate of the motion parameters $p$? The usual way to do this
  is to use least squares, i.e., to minimize the sum of squared residuals
  $$E_{LS} = \sum{\lVert{r_i}\rVert^2} = \sum{\lVert{f(x_i,p)-x'}\rVert^2},$$
  where $r_i$ is the residual between the measured location $x_i'$ and its corresponding current predicted location $f(x_i; p)$.
  
  Many of the motion models presented in Section 2.1.2 and Table 2.1, i.e., translation,
  similarity, and affine, have a linear relationship between the amount of motion $\Delta{x} = x'-x$ and the unknown parameters $p$,
  $$\Delta{x}=x'-x=J(x)p$$
  where $J=\partial{f}/\partial{p}$ is the Jacobian of the transformation $f$ with respect to the motion parameters $p$ (see Table 6.1). In this case, a simple linear regression (linear least squares problem)
  can be formulated as
  $$E_{LSS} = \sum{\lVert{J(x_i)p - \Delta{x_i}}\rVert^2}$$


Question:
In the last equation, what is the point of trying to minimize the difference between $J(x_i)p$ and $\Delta{x_i}$? What is a higher-level interpretation?
The set of feature points $\boldsymbol{x}_i$ and $\boldsymbol{x}^{\prime}_i$ is given, and you want to optimize the parameters $\boldsymbol{p}$ of a given planar transformation $\boldsymbol{f}(\boldsymbol{x};\boldsymbol{p})$ such that it predicts the actual transformation from $\boldsymbol{x}_i$ to $\boldsymbol{x}^{\prime}_i$ with minimum error. In this way, the parametric transformation optimally describes (and predicts) the actual transformation.

One way to do this is to minimize the error between the actual difference $\Delta\boldsymbol{x}_i=\boldsymbol{x}^{\prime}_i-\boldsymbol{x}_i$ and the difference predicted by the chosen transformation. The predicted difference is formulated in terms of the Jacobian of the transformation $\boldsymbol{f}(\boldsymbol{x};\boldsymbol{p})$ in order to get a linear least squares problem (which is easy to solve): ${\Delta\widehat{\boldsymbol{x}}_i}=\boldsymbol{J}(\boldsymbol{x}_i)\boldsymbol{p}$.

So the error to be minimized is given by

$$\sum_i\left[{\Delta\widehat{\boldsymbol{x}}_i}-\Delta\boldsymbol{x}_i \right]^2=\sum_i\left[\boldsymbol{J}(\boldsymbol{x}_i)\boldsymbol{p}-\Delta\boldsymbol{x}_i \right]^2\tag{1}$$

Chosing the parameters $\boldsymbol{p}$ that minimize $(1)$ minimizes the average squared error between the actual feature point difference $\Delta\boldsymbol{x}_i$ and the one predicted by the transformation $\boldsymbol{f}(\boldsymbol{x};\boldsymbol{p})$.