  Robust estimation of camera motion under the presence of outlier noise is a
fundamental problem in robotics and computer vision. Despite existing efforts
that focus on detecting motion and scene degeneracies, the best existing
approach that builds on Random Consensus Sampling (RANSAC) still has
non-negligible failure rate. Since a single failure can lead to the failure of
the entire visual simultaneous localization and mapping, it is important to
further improve robust estimation algorithm. We propose a new robust camera
motion estimator (RCME) by incorporating two main changes: model-sample
consistence test at model instantiation step and inlier set quality test that
verifies model-inlier consistence using differential entropy. We have
implemented our RCME algorithm and tested it under many public datasets. The
results have shown consistent reduction in failure rate when comparing to
RANSAC-based Gold Standard approach. More specifically, the overall failure
rate for indoor environments has reduced from 1.41% to 0.02%.
