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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<meta name="description"
content="ROMAN: Open-Set Object Map Alignment for
Robust View-Invariant Global Localization">
<meta name="keywords" content="Nerfies, D-NeRF, NeRF">
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<title>ROMAN: Open-Set Object Map Alignment for
Robust View-Invariant Global Localization</title>
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<h1 class="title is-1 publication-title">ROMAN: Open-Set Object Map Alignment for
Robust View-Invariant Global Localization</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://github.com/mbpeterson70">Mason Peterson</a><sup>1</sup>,
<span class="author-block">
<a href="https://acl.mit.edu/people/yixuany">Yi Xuan Jia</a><sup>1</sup>,</span>
<span class="author-block">
<a href="https://www.tianyulun.com/">Yulun Tian</a><sup>1,2</sup>,
</span>
<span class="author-block">
<a href="https://www.annikathomas.com">Annika Thomas</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="https://www.mit.edu/~jhow/">Jonathan How</a><sup>1</sup>
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><sup>1</sup>Massachusetts Institute of Technology</span>
<span class="author-block"><sup>2</sup>University of California, San Diego</span>
</div>
<div class="column has-text-centered">
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<span>arXiv</span>
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<p class="caption">Pair of segment submaps matched by two
robots traveling in opposite directions in an off-road environment.
Associated segments found by the proposed method are connected by lines and projected
onto the image plane. (Top) Each pair of associated segments is
drawn with the same color. The remaining, unmatched segments are
shown in random colors and all other background points are shown
in gray. (Bottom) The same associated segments and their convex
hulls are visualized in the original image observations.</p>
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<h2 class="title is-3">Abstract</h2>
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<p>
Global localization is a fundamental capability required for long-term and drift-free robot navigation.
However, current methods fail
to relocalize when faced with significantly different viewpoints.
We present ROMAN (Robust Object Map Alignment Anywhere), a robust global localization method capable of localizing in challenging and diverse environments based on creating and aligning maps of open-set and view-invariant objects.
To address localization difficulties caused by feature-sparse or perceptually aliased environments, ROMAN formulates and solves a registration problem between object submaps using a unified graph-theoretic global data association approach that simultaneously accounts for object shape and semantic similarities and a prior on gravity direction.
Through a set of challenging large-scale multi-robot or multi-session SLAM experiments in indoor, urban and unstructured/forested environments,
we demonstrate that ROMAN achieves a maximum recall 36% higher than other object-based map alignment methods and an absolute trajectory error that is 37% lower than using visual features for loop closures.
</p>
</div>
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</div>
<section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<h2 class="title">BibTeX</h2>
<pre><code>@article{peterson2024roman,
author = {Peterson, Mason and Jia, Yi Xuan and Tian, Yulun and Thomas, Annika and How, Jonathan},
title = {ROMAN: Open-Set Object Map Alignment for Robust View-Invariant Global Localization},
journal = {arXiv preprint arXiv:2410.08262},
year = {2024},
}</code></pre>
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