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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd">
<html xmlns="http://www.w3.org/1999/xhtml">
<head>
<link rel="icon" type="image/png" href="data/seal_icon.png">
<title>GIRA3D RSS 2023 Tutorial</title>
<meta http-equiv="content-type" content="text/html; charset=utf-8" />
<link href="data/default.css" rel="stylesheet" type="text/css" />
<meta property='og:title' content='GIRA3D RSS 2023 Tutorial' />
<meta property='og:url' content='https://gira3d.github.io/' />
<meta property='og:image' content='https://gira3d.github.io/data/teaserim.png' />
<meta property="og:type" content="website" />
</head>
<body>
<div id="header">
<div id="logo">
<h1 class="logo-header">
<center>
GIRA: Gaussian Mixture Models for Inference and Robot Autonomy
</center>
</h1>
<h2 class="logo-header">
<center>
Robotics: Science and Systems XIX
</center>
<center>
Daegu, Republic of Korea
</center>
<br>
<center>Daegu Exhibition and Convention Center (EXCO)</center>
<center>July 14, 2023</center>
<center>1330 - 1700 KST</center>
<br>
<center>Room: 321 or <a href="https://roboticsconference.org/attending/virtual/"> Virtual</a></center>
</h2><br>
<h2 class="logo-header">
<center>
<span style="font-size:92%;color:#777;font-weight:normal">Half-Day Tutorial<br/>
</center>
<center>
<span style="font-size:92%;color:#777;font-weight:normal">Organizers: <a href="https://www.kshitijgoel.com/">Kshitij Goel</a> and <a href="https://www.ri.cmu.edu/ri-faculty/wennie-tabib/">Wennie Tabib</a></span><br/>
</center>
</h2><br>
<h2 class="logo-header">
<table border="0" align="center">
<td colspan="5" align="center"><span class="menubar">
[ <a href="index.html">Home</a> |
<a href="https://arxiv.org/pdf/2307.00071.pdf">Paper</a> |
<a href="./pages/schedule.html">Schedule</a> |
<a href="docs/index.html">Tutorials</a> |
<a href="https://github.com/gira3d">Github</a> ]
</span></td>
</table>
</h2> <br>
</div>
<div id="videoal">
<div class="video">
<video controls autoplay muted loop>
<source src="./data/Video-low-res.mp4#t=14" type="video/mp4">
</video>
</div>
<h3><center>GIRA enables compact, large-scale, high-fidelity mapping of complex environments.</center></h3>
</div>
<div id="content">
<h2>Tutorial Description</h2>
<p>
Heterogeneous human-robot field deployments are challenged by the need
to share high-resolution perceptual information over low-bandwidth
communication channels. Recent work in large-scale heterogeneous
deployment, like the DARPA Sub-T Challenge, has highlighted the need for
map compression techniques to facilitate information sharing and
increase the rate of autonomous tasks like exploration. Future
deployments of large-scale, diverse robot teams will rely on compact and
high-fidelity perceptual representations that also enable fundamental
robotic capabilities such as pose estimation, occupancy modeling, and
collision avoidance.
</p>
<p>
While state-of-the-art frameworks employ sparse or voxelized dense data, there
are limitations with respect to representational fidelity and memory
constraints, respectively. Recent works have leveraged Gaussian mixture models
for environment representation due to their advantages in enabling high-fidelity
and memory-efficient modeling and inference on real-world sensor data in diverse
environments. Most prior works that have leveraged these compact approximate
continuous belief distributions for environment modeling have not open-sourced
their GPU-accelerated implementations, which poses a barrier to broad adoption
by the general robotics community. Therefore, there lies a challenge with
respect to the accessibility of these formulations to technical experts.
</p>
<p>
This tutorial bridges these gaps in the state of the art by introducing GIRA, an
open-source framework for learning Gaussian mixture models with applications to
robot autonomy and inference. GIRA provides GPU-accelerated functions to learn
GMMs 10-100x faster as compared to CPU and Python implementations. The tutorial
will provide extensive coverage of GIRA’s API and demonstrate fundamental
robotic applications. The only prerequisite for the tutorial is familiarity with
Python.
</p>
<h2>References</h2>
<p>
This tutorial is based on the following publications.
<ol>
<li>W. Tabib, C. O’Meadhra, and N. Michael, “On-Manifold GMM
Registration,” IEEE Robotics and Automation Letters, vol. 3, no. 4, pp.
3805–3812, Oct. 2018, doi: 10.1109/LRA.2018.2856279.
</li>
<li>C. O’Meadhra, W. Tabib, and N. Michael, “Variable Resolution
Occupancy Mapping Using Gaussian Mixture Models,” IEEE Robotics and
Automation Letters, vol. 4, no. 2, pp. 2015–2022, Apr. 2019, doi:
10.1109/LRA.2018.2889348.
</li>
<li>W. Tabib, K. Goel, J. Yao, M. Dabhi, C. Boirum, and N. Michael,
“Real-Time Information-Theoretic Exploration with Gaussian Mixture Model
Maps,” in Robotics: Science and Systems XV, Jun. 2019. doi:
10.15607/RSS.2019.XV.061.
</li>
<li>W. Tabib, K. Goel, J. Yao, C. Boirum, and N. Michael, “Autonomous
Cave Surveying With an Aerial Robot,” IEEE Transactions on Robotics, pp.
1–17, 2021, doi: 10.1109/TRO.2021.3104459. <p style="color: darkred; display: inline">King-Sun Fu Memorial Best Paper Award Honorable Mention</p>
</li>
<li>K. Goel, W. Tabib, and N. Michael, “Rapid and High-Fidelity
Subsurface Exploration with Multiple Aerial Robots,” in Experimental Robotics,
B. Siciliano, C. Laschi, and O. Khatib, Eds., in Springer Proceedings in
Advanced Robotics. Cham: Springer International Publishing, 2021, pp. 436–448.
doi: 10.1007/978-3-030-71151-1_39.
</li>
<li>W. Tabib and N. Michael, “Simultaneous Localization and Mapping of
Subterranean Voids with Gaussian Mixture Models,” in Field and Service
Robotics, Singapore, 2021, pp. 173–187. doi:
10.1007/978-981-15-9460-1_13.
</li>
<li>K. Goel, N. Michael, and W. Tabib, “Probabilistic Point Cloud
Modeling via Self-Organizing Gaussian Mixture Models,” IEEE Robotics
and Automation Letters, vol. 8, no. 5, pp. 2526–2533, May 2023, doi:
10.1109/LRA.2023.3256923.
</li>
</ol>
<h2>Organizers</h2>
<div class="instructor">
<a href="https://www.kshitijgoel.com/">
<div class="instructorphoto"><img src="./data/kshitij.jpg"></div>
<div>Kshitij Goel</div>
</a>
</div>
<div class="instructor">
<a href="https://www.ri.cmu.edu/ri-faculty/wennie-tabib/">
<div class="instructorphoto"><img src="./data/wennie.jpg"></div>
<div>Wennie Tabib</div>
</a>
</div>
<br>
<div style="clear: both;"> </div>
</div>
</body>
</html>