<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>C++ on Rishika Bera</title><link>https://rishika2024.github.io/tags/c++/</link><description>Recent content in C++ on Rishika Bera</description><generator>Hugo -- gohugo.io</generator><language>en</language><managingEditor>berarishika@gmail.com (Rishika Bera)</managingEditor><webMaster>berarishika@gmail.com (Rishika Bera)</webMaster><copyright>Rishika Bera</copyright><lastBuildDate>Fri, 10 Jul 2026 14:15:05 +0700</lastBuildDate><atom:link href="https://rishika2024.github.io/tags/c++/index.xml" rel="self" type="application/rss+xml"/><item><title>Robotic 3D Printing with a Meca500</title><link>https://rishika2024.github.io/posts/meca500_3d_printer/</link><pubDate>Fri, 10 Jul 2026 14:15:05 +0700</pubDate><author>berarishika@gmail.com (Rishika Bera)</author><guid>https://rishika2024.github.io/posts/meca500_3d_printer/</guid><description>&lt;style&gt;
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&lt;h2 class="relative group"&gt;Overview
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&lt;p&gt;I built a ROS 2 package that drives a Meca500 6-DOF arm (5 μm resolution) as a 3D printer through MoveIt 2. A custom &lt;code&gt;ros2_control&lt;/code&gt; hardware interface bridges the Meca500&amp;rsquo;s proprietary TCP API to MoveIt 2, so real trajectory planning and execution run on the physical arm. A mounted Ender3 extruder handles filament feed and heating over USB serial — the arm does all of the XYZ motion.&lt;/p&gt;</description></item><item><title>EKF SLAM with unknown data association</title><link>https://rishika2024.github.io/posts/slam/</link><pubDate>Sun, 22 Mar 2026 14:15:05 +0700</pubDate><author>berarishika@gmail.com (Rishika Bera)</author><guid>https://rishika2024.github.io/posts/slam/</guid><description>&lt;h2 class="relative group"&gt;Overview
 &lt;div id="overview" class="anchor"&gt;&lt;/div&gt;
 
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&lt;p&gt;Built a complete EKF SLAM pipeline from scratch in C++ using ROS 2, simulated on a TurtleBot3. The robot localizes itself and maps cylindrical landmarks simultaneously using wheel odometry and 2D LiDAR. Three robots are visualized: red (ground truth, known only to the simulator), blue (odometry estimate, which drifts over time), and green (SLAM corrected estimate). The video shows the robot driving a closed path with sensor noise and limited detection range. As odometry drifts, the EKF corrects it by fusing landmark observations, keeping the green path close to ground truth while blue diverges. Landmark estimates in green converge toward their true positions with repeated observations.&lt;/p&gt;</description></item></channel></rss>