Spatial Resection for Solving Camera Exterior Orientation Parameters

Resource Overview

This method solves camera exterior orientation elements using spatial resection with the following variables: % x,y control point image coordinates % X,Y,Z control point ground coordinates % f focal length % X0,Y0,Z0,a,b,c six exterior orientation parameters % x0,y0,-f interior orientation elements (principal point coordinates) % cha,chb,chc correction values for exterior angular elements % count iteration counter % R rotation matrix % A linearized partial derivative coefficient matrix % L constant term matrix % M0 exterior orientation parameters matrix % M1 exterior orientation correction parameters matrix. The implementation typically involves iterative least-squares adjustment with coordinate transformation using rotation matrices.

Detailed Documentation

When using spatial resection to solve camera exterior orientation parameters, we need to consider the following variables:

- x, y: control point image coordinates

- X, Y, Z: control point ground coordinates

- f: focal length

- X0, Y0, Z0, a, b, c: six exterior orientation parameters (position and attitude angles)

- x0, y0, -f: interior orientation elements (principal point coordinates)

- cha, chb, chc: correction values for exterior angular elements (used in iterative refinement)

- count: iteration counter (tracks convergence progress)

- R: rotation matrix (constructed from attitude angles a, b, c using Euler angles or rotation matrices)

- A: linearized partial derivative coefficient matrix (Jacobian matrix in the least-squares adjustment)

- L: constant term matrix (residuals between observed and computed coordinates)

- M0: exterior orientation parameters matrix (stores current parameter estimates)

- M1: exterior orientation correction parameters matrix (contains parameter adjustments computed in each iteration)

These variables form the essential components for implementing spatial resection to determine camera exterior orientation parameters, typically through an iterative least-squares solution that minimizes the differences between observed and projected image coordinates.