fix: moved vaquita, fixed maya styling and image

projects/old_projects/maya-achaeology/maya-achaeology.md

@@ -28,7 +28,7 @@ This project is divided into 3 main parts:
 
 The Tunnel Mapping project aims to lower the cost of digital documentation by experimenting with data collection methods. These methods include stereo-panoramic cameras, LiDAR, and experimental remote sensing systems based on the Microsoft Kinect camera or the Google Tango tablet. In prior expeditions to Guatemala, we brought a ground-based LiDAR system for high resolution scans of these large excavated temples. One method we are particularly excited about is Structure from Motion (SfM), a low cost technique for generating 3D models using photos from a traditional camera. This group is seeking highly motivated individuals to build data collection infrastructure for expeditions.
 
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 <iframe allowfullscreen="allowfullscreen" frameborder="0" height="315" loading="lazy" src="https://www.youtube.com/embed/pTD_uXEwNHw" width="540"></iframe>
 
@@ -66,7 +66,7 @@ Created using Point clouds from the Faro Focus 3D 120, Lieca BLK360
 ### Terrestrial Light Detection and Ranging (LiDAR)
 The process for Terrestrial LiDAR Scanning (TLS) generally involves setting the desired scan density, which in affect increases the number of points collected and time needed per scan. In the case of the Lieca BLK360 TLS, medium quality was sufficient for our purposes and took 3 minutes per scan. Each scan occurred more than 1 meter apart depending on if all the environment’s geometry could be reached from the previous scans location. The set of individual point clouds obtained was then registered using visual alignment in Lieca’s propriety software Cyclone Register 360. The output of all this is several standard file format .ptx files all in alignment with each other, which can then be brought to external software for further 3D point cloud and mesh processing.
 
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+![]({{"assets/projects-old_projects-maya-archaeology-LiDARm71insideoutside-350x173.png" | absolute_url}})
 
 ### Structure from Motion (SfM)
 SfM point cloud derived models are able to store accurate RGB data at a higher resolution, but with lower geometric accuracy. Since RGB information collected during SfM have greater accuracy, they can be useful in projecting color information back onto the Mesh generated by LIDAR. We can do Fine registration with the Iterative Closet Point (ICP) algorithm on the 2 Models to share the same coordinate space.