SO503 Spring 2017

Matlab and LIDAR Point Clouds/3D graphics

LIDAR point clouds come in a wide variety of formats.  The simplest are ASCII text files, with three values per line, x, y, and z.  It is quite likely, however, that that files will be too big to deal with in Excel, at least the older versions of the program.  There are two variants:

For serious use in computer mapping, the most common format is LAS, which is binary and substantially more compact that these ASCII files.  LAS files can have hundreds of millions of points (up to 15 points per square meter, and I have now seen one with 65 points per square meter), and be gigabytes in size.

You will use the following Point Clouds.  Note that the appropriate grid size to create varies with the data set characteristics:

We will use 3D graphics later to look at the 3D distribution of water properties recorded by a CTD.

We will be doing Lab 1 in the reading, which starts on page 32.  You have the data files already, so you can jump to the middle of page 35 for directions on using Matlab. We do not have an m file to start with, but you can use the code on page 38 as a starting point.  Note that these procedures will be happiest with data imported as a matrix, which is a little different than we have used to date when we have used vectors.

You will ultimately produce Figures for all four datasets; if your Matlab is slow, you might think about which data set will be best (fewest or most points?) to use while you are debugging your code.  You must use the same code, and just change a few variables at the beginning to deal with the different data sets.

Create the following figures (you might have to check the help on the parameters to use with each; there are samples of the output from all 5 below):

You can open the first three files in MICRODEM with the Point Clouds menu choice.  For the vineyard file, you need so to insert column names in a new first line.

Turn in for this lab:

Links for this lab only available within USNA

Matlab figures for surfl, surface, surfc, trimesh, and scatter3 for the smallest data set

MICRODEM Images for the two larger data sets