https://mshields.name/tags/remote-sensing/ Recent content in remote sensing on Matthew Shields Hugo -- gohugo.io © 2022 - 2026 Matthew Shields Sun, 01 May 2022 00:00:00 +0000 https://mshields.name/blog/2022-05-01-setting-up-a-georeferenced-rtk-base-station/ Sun, 01 May 2022 00:00:00 +0000 https://mshields.name/blog/2022-05-01-setting-up-a-georeferenced-rtk-base-station/ Preamble In my previous post I detailed how to use RINEX data to create a sub-decimeter level position solution. Using RINEX data in this way from government sources ties you to an agreed upon reference datum. With your own base station this can be problematic as the accuracy of the established position of the station will likely not be geographically accurate. This then leads to repeatable rover solutions but they will have some offset versus many other georeferrenced data sets, eg. https://mshields.name/blog/2022-03-10-registration-methods-comparison/ Thu, 24 Mar 2022 00:00:00 +0000 https://mshields.name/blog/2022-03-10-registration-methods-comparison/ 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. https://mshields.name/blog/2022-02-23-extracting-road-markings-from-pointcloud-data/ Sat, 26 Feb 2022 00:00:00 +0000 https://mshields.name/blog/2022-02-23-extracting-road-markings-from-pointcloud-data/ 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.