Can You Trust Your Pose? Confidence Estimation in Visual Localization

TL;DR

This paper introduces a novel confidence estimation method for visual pose estimation, enabling reliability assessment and potential accuracy improvements across diverse datasets and pipelines with minimal computational overhead.

Contribution

It proposes a new confidence measure for visual localization that is adaptable, lightweight, and can enhance existing pose estimation accuracy.

Findings

The confidence measure is applicable to indoor and outdoor datasets.

It can improve the accuracy of existing pose estimation pipelines.

The approach adds negligible computational cost.

Abstract

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.