Presentation by Tessie Hartjes and Iris Huijben of team Blue Jay Eindhoven for the meetup Girl Code meets IOT. https://www.bluejayeindhoven.nl/
Topic: Blue Jay, the first domestic drone
Tessie: Co-founder of team Blue Jay and Team Manager
Iris: Developer specialised in object recognition and navigation
IAC 2024 - IA Fast Track to Search Focused AI Solutions
How neural networks can make a drone your next best friend, by Tessie Hartjes and Iris Huijben
1. How neural networks can make a drone
your next best friend
Tessie Hartjes
MASTER Electrical Engineering
Electromechanics & power electronics
BACHELOR Innovation Science
Track: Energie
Iris Huijben
MASTER Electrical Engineering
Track: Care and Cure - Signal Processing
BACHELOR Pyschology & Technology
Track: Robotica
How neural networks can make a drone
your next best friend
Blue Jay Eindhoven
10-08-2016
5. www.bluejayeindhoven.nl
Biggest challenge
04
Accurate indoor navigation
Flying indoor and between people = no room for error!
- The drone will fly more stable when it ‘flies itself’ (autonomous)
- But, existing solutions aren’t accurate enough
6. www.bluejayeindhoven.nl
Our solution
04
Visible light communication: cm exact indoor navigation
Online database
- Every LED lamp has its own recognizable frequency
- An online database holds the position of every lamp and is
accessed by the drone
- The camera of your smartphone registers the composed light and
calculates your position with respect to the known configuration of
the lamps
Unique and precise solution within robotics domain but one problem: people only walk in the x- and y-plane need
for sensor fusion
7. www.bluejayeindhoven.nl
Our solution
04
Sensor fusion
LiDAR
Light detection and ranging sensor
Altitude measurement based on timing
differences of laser beams
Optical flow
Relative x and y displacement
A camera that ‘sees’ relative movement through
image displacement
12. www.bluejayeindhoven.nl
Challenges & user cases
09
Object avoidance
Health care
Recognizing new
people and objects
Noise reduction
Assistance / guidance
Fire safety
In mijn bachelor eindproject ben ik vorig jaar bezig geweest met herkenning van draaiingshoek van een gezicht.
Ik heb toen verschillende soorten neurale netwerken getraind op databases van foto’s van gezichten met meerdere draaiingshoeken en verlichtingshoeken.
In deze grafiek zie je een van de betere resultaten. Hier heb ik ook gebruik gemaakt van de grootste trainingsdatabase met de meeste variantie.
Nu aan jullie de vraag, hoeveel foto’s heb ik hiervoor gebruikt denken jullie?