

Dyson 360 eye robot vacuum - sheri
https://www.dyson360eye.com/

======
tonylemesmer
I worked on this product back in 2002-2005. I developed the initial wheel
system for this form factor of product and early prototypes of their digital
motor once we realised we'd need a very high power density compressor motor.
Sadly the battery technology has taken a long time to get to the point where
it can now give a useable run time, even when the vacuum only consumes 100W.
Also the prices of other components, high power embedded CPUs, cameras and
sensors have reduced dramatically since then. It uses an intelligent algorithm
to maximise the potential of the runtime, meaning that it tries to elminiate
running over the same patch of floor more than once. This is what it uses the
360 camera for and SLAM image processing that I still don't fully understand
:) The chap with grey hair switching it off at the end of the teaser video is
the brains behind all the navigation and image processing software, Mike
Aldred, a very clever guy.

[edit] missed a word

~~~
jfim
Simultaneous localization and mapping (SLAM) essentially refers to various
algorithms to determine egomotion (how much a robot moves in an environment)
using sensors, while building a map at the same time.

Essentially, at each time step, the algorithm senses its environment and
checks how much it differs from the previous time step, and figures out if it
saw any new features to add to the map and how much the correlated features
between the two time steps moved, to infer egomotion. This doesn't have to be
necessarily with cameras, it can also be done with laser rangefinders and
other relatively accurate sensors.

Monocular SLAM (MonoSLAM, also the name of a well known paper) is SLAM done
with a single camera, which makes the problem harder than with two cameras.
With two cameras affixed to a rigid frame and known characteristics, it's
possible to determine the 3D position of any given feature that is seen by
both cameras at the same time. With a single camera, however, it's trickier
because only the angle of a given feature can be determined, not its 3D
position, so an optimization step has to be done to determine what the
likeliest solution to the problem is.

There's also more to read on the relevant Wikipedia article, at
[http://en.wikipedia.org/wiki/Simultaneous_localization_and_m...](http://en.wikipedia.org/wiki/Simultaneous_localization_and_mapping)

~~~
tonylemesmer
OK so my understanding is pretty much the gist of that. See how you've moved
by comparing features extracted from a series of images taken over time. I
just don't understand the maths :)

The reason we went with a single camera is lack of space. As you can see from
some of the imagery of the product, the camera stack is a huge proportion of
the machine. Also when the algorithms were being developed in the early 2000's
cameras were still expensive bits of kit. I seem to remember the first one
being 1024x1024 resolution, pretty poor for photography, but good enough for
feature mapping with SLAM.

------
teekert
I'm seeing the markup language, not the actual text... FF 32 on Win7. I.e.,
the whole site is filed with:

 __ _[en-gb|Vision_headline]_ _____[en-gb|Vision_subhead]_ __

 __ _[en-gb|Vision_body]_ __

------
chrisan
Nice video showing how its system works:
[https://www.youtube.com/watch?feature=player_embedded&v=oguK...](https://www.youtube.com/watch?feature=player_embedded&v=oguKCHP7jNQ)

------
jadc
Neat. I wonder how it will compare to the Roomba. I own a Roomba 770 and am
pretty happy with it so far. I am assuming the filter is probably going to be
better on the Dyson.

The 360 is supposed to come out in 2015 and only in Japan at first so no
regrets about purchasing the Roomba :)

------
chrisblackwell
Wow, I really want that Mac Pro, I mean vacuum ([http://www.apple.com/mac-
pro/](http://www.apple.com/mac-pro/))

