crop_scans.r

# This program takes a raw scan (of the whole lab) and crops 
# it down to only the fire platform and  the fuel bed. 
# It also denoises the cropped scan.
# The output is a cropped, denoised .las file, saved to the 
# output.path.

# note: this program finds the fire platform by calculating 
# the median x and y coordinates of all points in the cloud.
# This program may or may not work when experiments have 
# different geometries (i.e. when the platform is not in the 
# middle of the raw scan) 

require("lidR")

# USER: type the path to your input .las file here:
input.path <- "C:/Users/js81535/Desktop/lidar_exploration/dry_nocones_rep1_pre.las"

# USER: type the path and file name for you output here:
output.path <- "C:/Users/js81535/Desktop/lidar_exploration/my_new_las.las"

# load the scan
las <- readLAS(input.path)

# find the median location of all the points in the scan
med.x <- median(las$X)
med.y <- median(las$Y)
med.z <- median(las$Z)

print(c(med.x, med.y, med.z))

# cut out a 2m square around the median location
top <- med.y + 1
right <- med.x + 1
bottom <- med.y - 1
left <- med.x - 1

trimmed.las <- clip_rectangle(las, xleft = left, ybottom = bottom, xright = right, ytop = top)

#plot(trimmed.las)

# crop out the cieling
ground.las <- filter_poi(trimmed.las, Z < 1)

# plot(ground.las)

# crop out the floor
platform.las <- filter_poi(ground.las, Z > -1.4)

# classify noise points and trim them
platform.las <- classify_noise(platform.las, sor(k=10, m=2))
platform.las <- filter_poi(platform.las, Classification != 18)

plot(platform.las)

# write the output to a new file
writeLAS(platform.las, output.path)