# Metric Monocular Localization Using Signed Distance Fields

arXiv: https://arxiv.org/abs/2003.14157 · HTML: https://mlsanity.com/paper/2003.14157/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2020.

## Built on by (most cited first, 1 of 1)

- [3D Surfel Map-Aided Visual Relocalization with Learned Descriptors](https://mlsanity.com/paper/2104.03856/) (2021)

## Abstract (arXiv)

Metric localization plays a critical role in vision-based navigation. For overcoming the degradation of matching photometry under appearance changes, recent research resorted to introducing geometry constraints of the prior scene structure. In this paper, we present a metric localization method for the monocular camera, using the Signed Distance Field (SDF) as a global map representation. Leveraging the volumetric distance information from SDFs, we aim to relax the assumption of an accurate structure from the local Bundle Adjustment (BA) in previous methods. By tightly coupling the distance factor with temporal visual constraints, our system corrects the odometry drift and jointly optimizes global camera poses with the local structure. We validate the proposed approach on both indoor and outdoor public datasets. Compared to the state-of-the-art methods, it achieves a comparable performance with a minimal sensor configuration.