artificial intelligence for securing navigation data in
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ARTIFICIAL INTELLIGENCE FOR SECURING NAVIGATION DATA IN
CHALLENGING SITUATIONSLaura Ruotsalainen, Associate Professor
Department of Computer Science
03/12/2019 1Laura Ruotsalainen, Department of Computer Science
52 YEARS OF EXCELLENCE
▪ Department of Computer Science
▪ Leading institution in Computer Science in Finland
▪ THE #93 (2018)
▪ The number of professors is 28
▪ Core CS and Data Science
▪ Algorithms
▪ AI
▪ Networking
▪ Software
03/12/2019 2
• Areas where satellite signals are degraded
• urban areas
• indoors
• Intentional interference of satellite positioning
• Autonomous vehicles
• UAVs, pedestrians
• Everything must work in one small, low-costdevice with limited user interaction
08/05/2019 3
NAVIGATION CHALLENGES WE ADDRESS
4
Indoor Navigation
SMART CITY 2035• Safety
• Environmental impacts
• Equality in mobility
• New business opportunities
Indoor Navigation
Market
08/05/2019 5
WLAN
RF Signals
Cellular network
& Digital TV
BluetoothRFID/
NFC
Accelerometers
Gyroscopes
Camera
Digital
compasses
Sensors
Satellites
GNSS
Solution is to fuse
adaptively suitable
positioning means
Computer
vision
5G
SOLUTIONS FROM DATA SCIENCE
6
Recursive
Bayesian
Estimation
Kalman filtering
Particle filtering
Cooperative
positioning
Statistical error
modellingMachine Learning
Recognizing
environment
Recognizing motion
Route prediction
Improving
measurements
• visual
• radio signal
COLLABORATIVE AUGMENTED NAVIGATION FOR DEFENCE OBJECTIVES (CANDO)
• Situational awareness and blue force tracking during urban counter terrorism operations
• navigate in an indoor environment, retaining room level accuracy over a 10-minute period
• Combining
• state of the art cooperative navigation technology
• state of the art pedestrian navigation and computer vision technology
• Funded by NATO Science for Peace and Security 2018-2019
3.12.2019 7
DETECTING DYNAMIC OBJECTS
• RealSense cameras for visual navigation
• Stereoscopic, subpixeld disparity accuracy
• Computer vision => bundle adjustment for motion
• Dynamic objects = human disturb computations
• Already trained human detectors don’t work
• Transfer learning for augmenting the data and classification developed with general data
03/12/2019Laura Ruotsalainen, Department of Computer Science 8
SEAMLESS PEDESTRIAN INDOOR /OUTDOOR NAVIGATION (SENT)
• Sensor fusion for seamless indoor / outdoor navigation
• Composing a Test-Bed with
• Realsense camera => SLAM
• Inertial sensors
• GNSS radio front-end, uBlox receiver, Android raw measurements
• WiFi RTT, Bluetooth
• Reference solution (high-grade IMU, professional antenna)
• Computing fused navigation solutions with differentmeasurement combinations
• Project funded by European Space Agency (ESA) 2019-2020
08/03/20199
IMPROVING INDUSTRIAL PROCESSES WITHAI
• Simultaneous Localization and Mapping (SLAM), Machine Learning for improved feature detection, dynamic objects
• Reflective surfaces
• Low-cost equipment
• Sensor fusion, integrity monitoring (detecting errors and failures)
• Deep learning for detecting humans and other movingobjects in the area
• Project funded by a donation from Konecranes 2020-2012
03/12/2019Laura Ruotsalainen, Department of Computer Science 10
08/05/2019 11
RAAS
• Autonomous traffic needs
• Seamless use of satellite positioning and othertechnologies
• Low latency in transmitting information => 5G
• Improved radio (5G) and visual positioning
• 5G-assisted Ground-based Galileo-GPS receiver Group with Inertial and Visual Enhancement (5GIVE)
• Project funded by European Space Agency(ESA) 1.3.2019 – 28.2.2020
• Collaboration with FGI
12
SEAMLESS ADAPTIVE NAVIGATION
• Using 5G signals for
• transmitting data for GNSS precise positioning (PPP, RTK) with low latency
• computing ranges between users for cooperative positioning
• Testing in Otaniemi
• Ranging using UWB and comparing to 5G signals by simulating
• Next step is research using 5G mmWave for positioning
13
5GIVE - UH
COMPUTER VISION FOR AIR NAVIGATION -SAFETY FIRST
• SLAM for UAV navigation in gnss challenging environments
• Deep learning methods needed for robustness
• Methods for assuring the integrity of computer vision based
drone operations are required
• Non-supervised machine learning and statistical
failure analysis for studying failure patterns
• Safety risk quantification
• Deep learning methods for improving
integrity monitoring
• Funding still pending
3.12.2019
Intel RealSense D435:
Global shutter
Depth computation
• Post-doc for 2 years (starting asap)
• PhD in Computer vision (preferablySLAM) or Deep Learning (preferablyobject recognition or semanticsegmentation)
• Good knowledge on the other
• Tasks: innovative and exciting research, collaboration withthe industry
• Coordinator for BF projectpreparation for 6 months (starting1/2020)
• Very good presentation skills, oral and written
• In Finnish and English
• Tasks: communication with companiesand authorities, arranging smallworkshops, preparing written material(proposal, presentation material)
• If technical background also researchtasks
03/12/2019Laura Ruotsalainen, Department of Computer Science 15
WE ARE HIRING!
60˚ 10 1.2 N, 24˚ 57 18 E
03/12/2019Laura Ruotsalainen, Department of Computer Science 16
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