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Hovermap-Zoe user manual
Release date: 01 Nov 2021
Revision: 1.0 15
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separate walking scan is a perfectly viable solution. The data from the walking scan can be merged with
the overall data later on. Only operate to your capabilities, and always have a plan.
2.4.2 Point density and resolution
Point density and resolution are closely related, but are slightly different. Use these two factors to plan
your mission so that you achieve the desired result.
2.4.2.1 Point density
Point density refers to the number of points in an area. You can achieve greater point density by
spending more time mapping your target, as well as by maintaining your target in Hovermap's central
field of view.
The largest number of points are obtained directly in front of the Hovermap, so keep any areas of
interest in this zone to achieve the best results. Hovermap is also constantly recording points, so
spending more time over your targeted areas will increase your point density, even if you can't focus
Hovermap directly on your points of interest.
2.4.2.2 Resolution
When it comes to resolution, the closer Hovermap is to its target, the greater the accuracy of the points.
Capturing data at 50 m away from the target will produce a fuzzier point cloud than if you are only 10 m
away.
For this reason, we don’t recommend that you fly over features more than 50 m away.
If you are closer to your target, you are more likely to produce a clean point cloud the first time around,
without the need for a lot of cleaning in the post-processing stage.
2.4.3 Environmental features
Hovermap works best when it has a good number of distinct, geometric features to help it to navigate
around its environment. This is one of the most difficult concepts to understand when it comes to
operating with Hovermap, as there is no absolute definition of what defines a feature-rich environment,
and what doesn't.
The best strategy for planning a mission is to understand each end of the environmental spectrum, and
how Hovermap would perform in that situation. Some environments can be particularly challenging.
Examples include:
Flat and smooth areas without geometric 3D features, such as large outdoor parking lots, sports
fields, and lakes.