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    <title>LiDAR on Matthew Shields</title>
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    <description>Recent content in LiDAR on Matthew Shields</description>
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    <copyright>© 2022 - 2026 Matthew Shields</copyright>
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      <title>Registration Methods Comparison</title>
      <link>https://mshields.name/blog/2022-03-10-registration-methods-comparison/</link>
      <pubDate>Thu, 24 Mar 2022 00:00:00 +0000</pubDate>
      
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      <description>Preamble Registration is the technique of aligning two data sets by finding either a rigid or affine transformation, depending on the problem. In robotics this is often between two pointclouds but the technique can be useful for aligning any data sets especially those that contain spatial data, MRI scan images for example. The Wikipedia page for this topic is quite concise and well written.
Background Typically we represent transformations in 3D space as a matrix and translation vector that we apply to an input vector of coordinates to produce a transformed output vector of shifted coordinates.</description>
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      <title>Extracting Road Markings from Pointcloud Data</title>
      <link>https://mshields.name/blog/2022-02-23-extracting-road-markings-from-pointcloud-data/</link>
      <pubDate>Sat, 26 Feb 2022 00:00:00 +0000</pubDate>
      
      <guid>https://mshields.name/blog/2022-02-23-extracting-road-markings-from-pointcloud-data/</guid>
      <description>Preamble This is the first post in a series looking back at past projects I have done but not shared publicly or documented in any way.
This was a piece of work from 2019 with the goal of extracting road markings from geo-referenced pointclouds. For those that don’t know, a geo-referenced pointcloud is created by taking a LiDAR and putting it on some kind of a rover vehicle, then taking all the observations of the LiDAR that were taken in the moving vehicle reference frame and converting them into the global “static” reference frame.</description>
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