  This article describes an algorithm that provides visual odometry estimates
from sequential pairs of RGBD images. The key contribution of this article on
RGBD odometry is that it provides both an odometry estimate and a covariance
for the odometry parameters in real-time via a representative covariance
matrix. Accurate, real-time parameter covariance is essential to effectively
fuse odometry measurements into most navigation systems. To date, this topic
has seen little treatment in research which limits the impact existing RGBD
odometry approaches have for localization in these systems. Covariance
estimates are obtained via a statistical perturbation approach motivated by
real-world models of RGBD sensor measurement noise. Results discuss the
accuracy of our RGBD odometry approach with respect to ground truth obtained
from a motion capture system and characterizes the suitability of this approach
for estimating the true RGBD odometry parameter uncertainty.
