<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Pro_projects | Callum Rhodes</title><link>https://callum-rhodes.github.io/pro_project/</link><atom:link href="https://callum-rhodes.github.io/pro_project/index.xml" rel="self" type="application/rss+xml"/><description>Pro_projects</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-gb</language><lastBuildDate>Mon, 01 Apr 2024 00:00:00 +0000</lastBuildDate><image><url>https://callum-rhodes.github.io/media/icon_hu881d0ee29fd03d49e84ba19efa0486fa_38556_512x512_fill_lanczos_center_3.png</url><title>Pro_projects</title><link>https://callum-rhodes.github.io/pro_project/</link></image><item><title>U-ARE-ME</title><link>https://callum-rhodes.github.io/pro_project/uareme/</link><pubDate>Mon, 01 Apr 2024 00:00:00 +0000</pubDate><guid>https://callum-rhodes.github.io/pro_project/uareme/</guid><description>&lt;ul>
&lt;li>U-ARE-ME provides globally consistent rotation estimates in Manhattan environments across sequences of RGB images, without the need for camera intrinsics.&lt;/li>
&lt;li>This is done by finding the rotation matrix that aligns the predicted surface normals to the principal directions of the scene.&lt;/li>
&lt;li>Even in non-Manhattan scenes, it can reliably estimate the global up-direction (i.e. the pitch &amp;amp; roll).&lt;/li>
&lt;/ul></description></item><item><title>3D gas distribution mapping</title><link>https://callum-rhodes.github.io/pro_project/3dgabp/</link><pubDate>Sun, 01 Oct 2023 00:00:00 +0000</pubDate><guid>https://callum-rhodes.github.io/pro_project/3dgabp/</guid><description>&lt;p>By formulating gas distribution mapping as an iterative least-squares problem and applying Gaussian belief propagation (GBP), we show how dense distribution maps can be inferred instantly using only posed sparse concentration measurments. This is demoed in a real-world scenarion onboard a Husky UGV carrying 3 gas concentration sensors at different heights.&lt;/p></description></item><item><title>ELROB 2022</title><link>https://callum-rhodes.github.io/pro_project/elrob/</link><pubDate>Sat, 01 Oct 2022 00:00:00 +0000</pubDate><guid>https://callum-rhodes.github.io/pro_project/elrob/</guid><description>&lt;p>ELROB: European land robotics trials. From small scale robotics to autonomous vehicles, the ELROB 2022 trials were a series of tests focussing on search and rescue, reconnaissance and autonomy. The team from Loughborough pushed the boundaries and developed a heterogeneous UGV and UAV combo for exploration and radiation mapping. The UGV acted as a drone delivery vehicle and allowed the sensing UAV to move in tight spaces until flying capablilities were required. Real time 3D radiation mapping was demonstrated for the first time here.&lt;/p></description></item><item><title>Auto-STE</title><link>https://callum-rhodes.github.io/pro_project/autoste/</link><pubDate>Wed, 01 Jun 2022 00:00:00 +0000</pubDate><guid>https://callum-rhodes.github.io/pro_project/autoste/</guid><description>&lt;p>Autonomous search for the source location of an airbourne chemical dispersion is performed entirely onboard a UAV within a GPS denied environment. SLAM output from a stereo camera is fed into the flight controller and a 3D octomap is used for collision free navigation. Information theoretic path planning is developed to guide future sensing locations by taking into account the uncertainty of the particle filter that is recursively estimating the parameters of the chemical dispersion.&lt;/p></description></item><item><title>EnRicH 2021</title><link>https://callum-rhodes.github.io/pro_project/enrich/</link><pubDate>Fri, 01 Oct 2021 00:00:00 +0000</pubDate><guid>https://callum-rhodes.github.io/pro_project/enrich/</guid><description>&lt;p>EnRicH: The European robotics hackathon hosted in Austria. The EnRicH 2021 event was held in Zwentendorf Nuclear Power Plant and provided a host of challenges including remote manipulation, autonomous exploration and radiation detection &amp;amp; mapping. Lead a team of researchers from Loughborough University in the radiation mapping challenge using a custom semi-autonomous UAV. In the challenge a large shaft had to be traversed and mapped, and a real radiation source found. This project used work from my PhD in order to perform the radiation mapping. Highlights can be found &lt;a href="https://www.youtube.com/watch?v=O7LYBUIATHw&amp;amp;list=PL8pZ0oVAzBk5tZt-sCvzJxr0iSRcVVJXZ&amp;amp;index=5&amp;amp;ab_channel=EuropeanRobotics" target="_blank" rel="noopener">here&lt;/a>.&lt;/p></description></item></channel></rss>