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index.html
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<!DOCTYPE HTML>
<html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
<title>Muhammad Shaheer</title>
<meta name="author" content="Muhammad Shaheer">
<meta name="viewport" content="width=device-width, initial-scale=1">
<link rel="stylesheet" type="text/css" href="stylesheet.css">
<link rel="icon" type="image/png" href="images/seal_icon.png">
</head>
<body>
<table style="width:100%;max-width:800px;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;"><tbody>
<tr style="padding:0px">
<td style="padding:0px">
<table style="width:100%;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;"><tbody>
<tr style="padding:0px">
<td style="padding:2.5%;width:63%;vertical-align:middle">
<p style="text-align:center">
<name>Muhammad Shaheer</name>
</p>
<p>I am a third year PhD student at University of Luxembourg advised by Holger Voos, working primarily on Situational Awareness for robots , 3D scene understanding, and SLAM.
Recently I'm interested in leveraging prior knowledge of environment, and how it could change how we interact with 3D.
<p>
Previously I finished my bachelor's in Mechtronics Engineering at NUST Pakistan, and completed my master's in Robotics at SDU Denmark, where I worked with Leon Bodenhagen on LiDAR SLAM.
<p style="text-align:center">
<a href="mailto:[email protected]">Email</a>  / 
<a href="https://twitter.com/mshaheerahmed">Twitter</a>  / 
<a href="https://github.com/shaheer34mts">Github</a>  / 
<a href="https://1drv.ms/b/s!AjCRjZ2rzLlNjwROreSdO03JYwQ4?e=QYjH8d">CV</a>  / 
<a href="https://scholar.google.com/citations?user=vjHnjjUAAAAJ&hl=en&oi=ao"> Google Scholar</a>
 / 
<a href="https://www.linkedin.com/in/muhammad-shaheer-279361121/">LinkedIn</a>
</p>
</td>
<td style="padding:2.5%;width:40%;max-width:40%">
<a href="images/shaheer_profile_pic.png"><img style="width:100%;max-width:100%" alt="profile photo" src="images/shaheer_profile_pic.png" class="hoverZoomLink"></a>
</td>
</tr>
</tbody></table>
<table style="width:100%;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;"><tbody>
<tr>
<td style="padding:20px;width:100%;vertical-align:middle">
<heading>Papers</heading>
</td>
</tr>
</tbody></table>
<table style="width:100%;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;"><tbody>
<!-- LERF -->
<tr onmouseout="lerf_stop()" onmouseover="lerf_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<!-- IMAGES -->
<div class="one">
<div class="two" id='lerf_under'>
<img src="images/bsg.png" width='180' id='lerf_under'/>
</div>
<video width=115% height=100% muted autoplay loop id="lerf_over">
<source src="images/ICRA_24_better_s_graphs.mp4" type="video/mp4">
Your browser does not support the video tag.
</video>
</div>
<!-- SCRIPT FOR HOVERING -->
<script type="text/javascript">
function lerf_start() {
document.getElementById('lerf_under').style.opacity = "0";
document.getElementById('lerf_over').style.opacity = "1";
}
function lerf_stop() {
document.getElementById('lerf_under').style.opacity = "1";
document.getElementById('lerf_over').style.opacity = "0";
}
lerf_stop()
</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<!-- TITLE -->
<a href="https://arxiv.org/abs/2310.00401"><papertitle>Better Situational Graphs by Inferring High-level Semantic-Relational Concepts</papertitle></a>
<br>
<!-- AUTHORS -->
Jose Andres Millan-Romera, Hriday Bavle, <b>Muhammad Shaheer</b>, Martin R. Oswald, Holger Voos, Jose Luis Sanchez-Lopez
<br>
<!-- CONFERENCE -->
<em>ICRA</em> 2024
<!-- LINKS -->
<br>
<a href="https://arxiv.org/abs/2310.00401">arXiv</a> / <a href="https://lerf.io">Video</a>
<p></p>
<!-- SUMMARY -->
We propose a Graph Neural Network (GNN) for learning high-level semantic-
relational concepts that can be inferred from the low-level factor graph
</td>
</tr>
<!-- TACVIS -->
<tr onmouseout="tacvis_stop()" onmouseover="tacvis_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<!-- IMAGES -->
<div class="one">
<div class="two" id='tacvis_under'>
<img src="images/is_graphs.png" width='180'/>
</div>
<video width=115% height=100% muted autoplay loop id="lerf_splash">
<source src="images/video_isgraphs.mp4" type="video/mp4">
Your browser does not support the video tag.
</video>
</div>
<!-- SCRIPT FOR HOVERING -->
<script type="text/javascript">
function tacvis_start() {
document.getElementById('lerf_splash').style.opacity = "0";
document.getElementById('tacvis_under').style.opacity = "1";
}
function tacvis_stop() {
document.getElementById('lerf_splash').style.opacity = "1";
document.getElementById('tacvis_under').style.opacity = "0";
}
tacvis_stop()
</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<!-- TITLE -->
<a href="https://arxiv.org/abs/2303.02076"><papertitle>Graph-based Global Robot Localization Informing Situational Graphs with Architectural Graphs</papertitle></a>
<br>
<!-- AUTHORS -->
<b>Muhammad Shaheer</b> , Jose Andres Millan-Romera, Hriday Bavle, Jose Luis Sanchez-Lopez, Javier Civera, Holger Voos
<br>
<!-- CONFERENCE -->
<em>IROS</em> 2023
<!-- LINKS -->
<br>
<a href="https://arxiv.org/abs/2303.02076">arXiv</a> / <a href="https://www.youtube.com/watch?v=thmBiDCgQUg&t=206s">Video</a>
<p></p>
<!-- SUMMARY -->
We develop a method for converting the plan of a building into an architectural graph (A-Graph). When the robot starts moving in an environment, and it estimates an online situational graph representation (S-Graph) of its surroundings. We develop a novel graph-to-graph matching method, to relate the S-Graph and A-graph.
</td>
</tr>
<!-- EVO-NERF -->
<tr onmouseout="evonerf_stop()" onmouseover="evonerf_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<!-- IMAGES -->
<div class="one">
<div class="two" id='evonerf_under'>
<img src="images/urchet1_floor_2_5_test.png" width='180'/>
</div>
<video width=115% height=100% muted autoplay loop id="evonerf_splash">
<source src="images/S_Graphs_P.mp4" type="video/mp4">
Your browser does not support the video tag.
</video>
</div>
<!-- SCRIPT FOR HOVERING -->
<script type="text/javascript">
function evonerf_start() {
document.getElementById('evonerf_splash').style.opacity = "0";
document.getElementById('evonerf_under').style.opacity = "1";
}
function evonerf_stop() {
document.getElementById('evonerf_splash').style.opacity = "1";
document.getElementById('evonerf_under').style.opacity = "0";
}
evonerf_stop()
</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<!-- TITLE -->
<a href="https://github.com/snt-arg/s_graphs_docker">
<papertitle>S-Graphs+: Real-Time Localization and Mapping Leveraging Hierarchical Representations</papertitle>
</a>
<br>
<!-- AUTHORS -->
Hriday Bavle, Jose Luis Sanchez-Lopez, <b>Muhammad Shaheer</b>, Javier Civera, Holger Voos
<br>
<!-- CONFERENCE -->
<em>RA-L</em> 2023, <b>Oral Presentation</b>
<!-- LINKS -->
<br>
<a href="https://arxiv.org/abs/2212.11770">arxiv</a>
/
<a href="https://openreview.net/forum?id=Bxr45keYrf">IEEE access</a>
<p></p>
<!-- SUMMARY -->
We present an evolved version of Situational Graphs, which jointly models in a single optimizable factor graph (1) a pose graph, as a set of robot poses,
and (2) a 3D scene graph, as a high-level representation of the environment that encodes its different geometric elements with semantic attributes
and the relational information between them.
</td>
</tr>
<!-- LUV -->
<!-- below is an example of doing the fancy mouseover thing -->
<!-- <tr onmouseout="hypernerf_stop()" onmouseover="hypernerf_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='hypernerf_image'><video width=100% height=100% muted autoplay loop>
<source src="images/hypernerf_after.mp4" type="video/mp4">
Your browser does not support the video tag.
</video></div>
<img src='images/hypernerf_before.jpg' width="160">
</div>
<script type="text/javascript">
function hypernerf_start() {
document.getElementById('hypernerf_image').style.opacity = "1";
}
function hypernerf_stop() {
document.getElementById('hypernerf_image').style.opacity = "0";
}
hypernerf_stop()
</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://hypernerf.github.io/">
<papertitle>HyperNeRF: A Higher-Dimensional Representation
for Topologically Varying Neural Radiance Fields</papertitle>
</a>
<br>
<a href="https://keunhong.com">Keunhong Park</a>,
<a href="https://utkarshsinha.com">Utkarsh Sinha</a>,
<a href="https://phogzone.com/">Peter Hedman</a>,
<strong>Jonathan T. Barron</strong>, <br>
<a href="http://sofienbouaziz.com">Sofien Bouaziz</a>,
<a href="https://www.danbgoldman.com">Dan B Goldman</a>,
<a href="http://www.ricardomartinbrualla.com">Ricardo Martin-Brualla</a>,
<a href="https://homes.cs.washington.edu/~seitz/">Steven M. Seitz</a>
<br>
<em>SIGGRAPH Asia</em>, 2021
<br>
<a href="https://hypernerf.github.io/">project page</a>
/
<a href="https://arxiv.org/abs/2106.13228">arXiv</a>
<p></p>
<p>Applying ideas from level set methods to NeRF lets you represent scenes that deform and change shape.</p>
</td>
</tr> -->
<!-- </tbody></table> -->
<!-- <table width="100%" align="center" border="0" cellspacing="0" cellpadding="20"><tbody>
<tr>
<td>
<heading>Service</heading>
</td>
</tr>
</tbody></table> -->
<!-- <table width="100%" align="center" border="0" cellpadding="20"><tbody> -->
<!-- <tr>
<td style="padding:20px;width:25%;vertical-align:middle">
<img src="images/cs188.jpg" alt="cs188">
</td>
<td width="75%" valign="center">
<a href="http://inst.eecs.berkeley.edu/~cs188/sp11/announcements.html">Graduate Student Instructor, CS188 Spring 2011</a>
<br>
<br>
<a href="http://inst.eecs.berkeley.edu/~cs188/fa10/announcements.html">Graduate Student Instructor, CS188 Fall 2010</a>
<br>
<br>
<a href="http://aima.cs.berkeley.edu/">Figures, "Artificial Intelligence: A Modern Approach", 3rd Edition</a>
</td>
</tr> -->
<!-- </tbody></table> -->
<table width="100%" align="center" border="0" cellspacing="0" cellpadding="20"><tbody>
<tr>
<td>
<heading>Personal Projects</heading>
</td>
</tr>
</tbody></table>
<!-- <table width="100%" align="center" border="0" cellpadding="0"><tbody>
<tr>
<td style="padding:0px;width:40%;vertical-align:middle;text-align:center" >
<a href="images/segway_pic.jpeg"><img src="images/segway_pic.jpeg" alt="segway" height="210"></a>
</td>
<td width="75%" valign="center">
<a href="https://github.com/kerrj/segway">Miniature self-balancing robot</a>, built from scratch using off-the-shelf parts and
able to follow waypoints using model-predictive control for balance and
<a href="https://www.ri.cmu.edu/pub_files/pub3/coulter_r_craig_1992_1/coulter_r_craig_1992_1.pdf">pure pursuit</a> for path following.
</td>
</tr>
<tr>
<td style="padding:20px;width:40%;vertical-align:middle;text-align: center;">
<a href="images/mapper_pic.jpeg"><img src="images/mapper_pic.jpeg" alt="mapping robot" height="182"></a>
<a href="images/map_pic.png"><img src="images/map_pic.png" alt="map" height="182"></a>
</td>
<td width="75%" valign="center">
<a href="https://github.com/kerrj/mapper">Indoor mapping robot</a> built from scratch with a cheap planar lidar which autonomously mapped my house using a
<a href="https://research.google/pubs/pub45466/">Google Cartographer</a>-inspired SLAM algorithm,
alongside a grid path planner for exploration and <a href="https://en.wikipedia.org/wiki/Dynamic_window_approach">DWA</a> controller for path following.
</td>
</tr> -->
</tbody></table>
<table style="width:100%;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;"><tbody>
<tr>
<td style="padding:0px">
<p>
Source code taken from <a href="https://jonbarron.info/">Jon Barron's site.</a>
</p>
</td>
</tr>
</tbody></table>
</td>
</tr>
</table>
</body>
</html>