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You are here: Home / **Front Page / LiDAR Feature – Random Points: Drones and LiDAR

LiDAR Feature – Random Points: Drones and LiDAR

February 19, 2015 By GISuser

A special LiDAR feature contribution from the February 2015 edition of LiDAR Magazine…

Random Points: Drones and LiDAR

From the article

As I write this article (early January 2015) the Federal Aviation Administration (FAA) has yet to release rules for the commercial use of small Unmanned Aerial Systems (sUAS) in the USA airspace. Meanwhile, hobby versions (technically identical to the commercial versions, in many cases) are flying off the shelf as more and more casual users realize the fun of flying and photographing.

In reality, commercial drones are not sitting in darkened hangers, waiting on rules. Hundreds, if not thousands of serious projects are being carried out on a daily basis with sUAS, predominantly carrying cameras. As discussed in several previous issues of LiDAR News, point clouds from dense image matching (DIM) is the enabling technology (along with the drone itself, of course) that has made all of this possible. Note that I use the term DIM to mean both the process and the product. Thus a DIM is a point cloud derived from the dense image matching process.

Drones are not so much taking away business from manned aerial survey missions as they are enabling aerial surveying where it was either not possible due to the site location, or simply was not economically feasible (the usual case). An example of a stockpile (modeled as a Triangulated Irregular Network, TIN) with an overhanging material conveyor (modeled as points) is depicted in Figure 1. These data were collected with a sUAS carrying a prosumer Sony NEX-5 camera.

Obviously these data are extremely valuable, not only for visualization but also for quantitative analysis such as volumetric computations. A close analysis of data from dense image matching (DIM), however, will reveal defects that may render some types of analysis inaccurate. For example, note in Figure 1 the general poor definition of the conveyor system (noise points, general “fuzziness”). For lack of a standard term, I refer to the degree of fidelity of a DIM to the true surface structure as “conformance.” A low conformance DIM is significantly deviating from the true surface whereas a high conformance DIM is closely representing the surface. There are examples in a DIM where there is simply no conformance at all, such as overhead wires.

Continue Reading – Author: Lewis Graham

Filed Under: **Front Page, LiDAR, SPATIAL MEDIA AUTHORS, Top Features, UAV/UAS Tagged With: 3D, drones, lidar, point cloud

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