GMMLoc: Structure Consistent Visual Localization with Gaussian Mixture Models

TL;DR

GMMLoc is a visual localization system that leverages Gaussian Mixture Models to incorporate structural priors, achieving high accuracy with minimal computational cost.

Contribution

This paper introduces GMMLoc, a novel method that integrates Gaussian Mixture Models into visual localization for improved accuracy and efficiency.

Findings

Achieves centimeter-level localization accuracy

Operates with trivial computational overhead

Demonstrates competitive performance against state-of-the-art methods

Abstract

Incorporating prior structure information into the visual state estimation could generally improve the localization performance. In this letter, we aim to address the paradox between accuracy and efficiency in coupling visual factors with structure constraints. To this end, we present a cross-modality method that tracks a camera in a prior map modelled by the Gaussian Mixture Model (GMM). With the pose estimated by the front-end initially, the local visual observations and map components are associated efficiently, and the visual structure from the triangulation is refined simultaneously. By introducing the hybrid structure factors into the joint optimization, the camera poses are bundle-adjusted with the local visual structure. By evaluating our complete system, namely GMMLoc, on the public dataset, we show how our system can provide a centimeter-level localization accuracy with only trivial computational overhead. In addition, the comparative studies with the state-of-the-art vision-dominant state estimators demonstrate the competitive performance of our method.