  This study presents a theoretical structure for the monocular pose estimation
problem using the total least squares. The unit-vector line-of-sight
observations of the features are extracted from the monocular camera images.
First, the optimization framework is formulated for the pose estimation problem
with observation vectors extracted from unit vectors from the camera
center-of-projection, pointing towards the image features. The attitude and
position solutions obtained via the derived optimization framework are proven
to reach the Cram\'er-Rao lower bound under the small angle approximation of
the attitude errors. Specifically, The Fisher Information Matrix and the
Cram\'er-Rao bounds are evaluated and compared to the analytical derivations of
the error-covariance expressions to rigorously prove the optimality of the
estimates. The sensor data for the measurement model is provided through a
series of vector observations, and two fully populated noise-covariance
matrices are assumed for the body and reference observation data. The inverse
of the former matrices appear in terms of a series of weight matrices in the
cost function. The proposed solution is simulated in a Monte-Carlo framework
with 10,000 samples to validate the error-covariance analysis.
