  Accurately estimating camera motion from image sequences poses a significant
challenge in computer vision and robotics. Many computer vision methods first
compute the essential matrix associated with a motion and then extract
orientation and normalized translation as inputs to pose estimation,
reconstructing the scene scale (that is unobservable in the epipolar
construction) from separate information. In this paper, we design a
continuous-time filter that exploits the same perspective by using the epipolar
constraint to define pseudo-measurements. We propose a novel polar symmetry on
the pose of the camera that makes these measurements equivariant. This allows
us to apply recent results from equivariant systems theory to estimating pose.
We provide a novel explicit persistence of excitation condition to characterize
observability of the full pose, ensuring reconstruction of the scale parameter
that is not directly observable in the epipolar construction.
