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