  We propose an automatic method for pose and motion estimation against a
ground surface for a ground-moving robot-mounted monocular camera. The
framework adopts a semi-dense approach that benefits from both a feature-based
method and an image-registration-based method by setting multiple patches in
the image for displacement computation through a highly accurate
image-registration technique. To improve accuracy, we introduce virtual inverse
perspective mapping (IPM) in the refinement step to eliminate the perspective
effect on image registration. The pose and motion are jointly and robustly
estimated by a formulation of geometric bundle adjustment via virtual IPM.
Unlike conventional visual odometry methods, the proposed method is free from
cumulative error because it directly estimates pose and motion against the
ground by taking advantage of a camera configuration mounted on a ground-moving
robot where the camera's vertical motion is ignorable compared to its height
within the frame interval and the nearby ground surface is approximately flat.
We conducted experiments in which the relative mean error of the pitch and roll
angles was approximately 1.0 degrees and the absolute mean error of the travel
distance was 0.3 mm, even under camera shaking within a short period.
