  Camera pose estimation in large-scale environments is still an open question
and, despite recent promising results, it may still fail in some situations.
The research so far has focused on improving subcomponents of estimation
pipelines, to achieve more accurate poses. However, there is no guarantee for
the result to be correct, even though the correctness of pose estimation is
critically important in several visual localization applications,such as in
autonomous navigation. In this paper we bring to attention a novel research
question, pose confidence estimation,where we aim at quantifying how reliable
the visually estimated pose is. We develop a novel confidence measure to fulfil
this task and show that it can be flexibly applied to different datasets,indoor
or outdoor, and for various visual localization pipelines.We also show that the
proposed techniques can be used to accomplish a secondary goal: improving the
accuracy of existing pose estimation pipelines. Finally, the proposed approach
is computationally light-weight and adds only a negligible increase to the
computational effort of pose estimation.
