<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Renlang Huang | RCL Group</title><link>https://rcl-group.github.io/authors/renlang-huang/</link><atom:link href="https://rcl-group.github.io/authors/renlang-huang/index.xml" rel="self" type="application/rss+xml"/><description>Renlang Huang</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Fri, 27 Jun 2025 00:00:00 +0000</lastBuildDate><image><url>https://rcl-group.github.io/media/icon_hu_c9b5ec8305c0ee34.png</url><title>Renlang Huang</title><link>https://rcl-group.github.io/authors/renlang-huang/</link></image><item><title>ROEVO Accepted by T-RO</title><link>https://rcl-group.github.io/blog/2025-roevo-tro/</link><pubDate>Fri, 27 Jun 2025 00:00:00 +0000</pubDate><guid>https://rcl-group.github.io/blog/2025-roevo-tro/</guid><description>&lt;p&gt;Our work
has been accepted by IEEE Transactions on Robotics (T-RO)! ROEVO introduces a robust visual odometry system using a novel &amp;ldquo;organized edges&amp;rdquo; feature representation for RGB-D cameras. By transforming disjoint edge pixels into sequentialized clusters, the method effectively retains textural and structural information and enables edge-level association across multiple frames via a co-visibility graph, achieving robust performance in diverse environments.&lt;/p&gt;</description></item></channel></rss>