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Low Cost GNSS and Computer Vision based data fusion solution for driverless vehicles

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TAXISAT PROJECT Low Cost GNSS and Computer Vision based data fusion solution for driverless vehicles Marc POLLINA pollina_at_m3systems.net – PowerPoint PPT presentation

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Title: Low Cost GNSS and Computer Vision based data fusion solution for driverless vehicles


1
Low Cost GNSS and Computer Vision based data
fusion solution for driverless vehicles
TAXISAT PROJECT
Marc POLLINA pollina_at_m3systems.net
2
Outline
  • Importance of ITS
  • In-vehicle systems Future Technologies
  • System Architecture
  • Results Analysis
  • Conclusions

3
Importance of ITS
  • The Global Market for ITS Technologies is
    estimated to grow to 50BN by 2020.
  • Automotive Industry is one of the most innovative
    sectors
  • Active Continuously monitor an aspect of the
    user, vehicle, environment or transport network
    and alert the user to potential danger, or
    intervene with the driving task to avoid danger
  • Passive These are crash mitigation or
    minimisation technologies that act to enhance the
    safety of the driver or other road users by
    minimising the severity.
  • Combined active and passive systems (CAPS) These
    systems monitor the environment, vehicle or
    driver for potential danger and then apply
    passive safety measures if a crash is deemed
    unavoidable

4
GNSS Sensor in Urban Area
Example of test case ( GUIDE Laboratory
Toulouse) Blue GNSS , Green reference ( PPK
high grade IMU)
5
GNSS Sensor in Urban Area
Example of test case ( GUIDE Laboratory
Toulouse) Blue GNSS , Green reference ( PPK
high grade IMU)
6
Future Technologies
  • Sensor Fusion is essential no sole positioning
    sensor covers all requirements and constraints
  • Combination of computer vision, 3D Maps and GNSS
    technologies are fostering new solutions not only
    for driving assistance but for unmanned vehicles

7
Future Technologies
  • GNSS new constellations new frequencies
  • New GNSS satellite constellations, signals, and
    associated frequency diversity is stimulating
    innovations in user equipment design leading to
    improved capabilities of positioning
  • 3D Maps city mapping
  • 3D city mapping has the potential to
    revolutionize positioning in challenging urban
    areas. Adding height information to street maps
    can be used to aid GNSS positioning for land
    vehicle and pedestrian navigation.
  • Computer vision intelligent camera
  • The major new navigation sensor of the next
    decade could well be the camera. Visual odometry,
    is a form of dead reckoning

8
Architecture
9
Architecture
Traditional Sensors Cost/Accuracy Trade off
  • Odometers for
  • Wheels speed
  • Front Axle orientation
  • Gyro
  • Optical
  • MEMS

10
Architecture
Position Sensors Cost/Accuracy Trade off
Trimble bullet III compact antenna - Low cost
and good gain
LEA-6T GPS/EGNOS receiver - Accurate, reliable
11
Architecture
Computer Vision Cost/Accuracy Trade off
FLEA 3, Point grey, stereo pair
SLAM Enhancing performance level compared to
usual INS Transversal displacements and
estimations of  velocity and orientation Matching
between a live map of the scene structure and a
new acquired image
FOLLOW THE LANE Improve security, reliability
and 24/7 operation possibility Extra feature
derived from ADAS to assist continuously the
cars control loops
12
Architecture
EDAS Connection Module
Local server - Hosting the EDAS client software
(EDAS server connection software) - Filtering
routine 3G communication - Communication between
the local server and the vehicle
13
Architecture
  • Tight Hybridization module composed of
  • An Inertial Navigation System (INS) which
    integrates the gyrometer/odometer data (100Hz)
  • A Navigation filter which updates and corrects
    the INS according to the measurements from the
    Vision or GNSS modules when available and valid
  • 3 platforms -gt Time synchronisation of
    measurement required

14
Real Time Scenario GeoPositioning
No Geo-Referenced information A-priori Unknown
scenario
Real World
Information Ratio
Real Distance / Location
Measured Information - GNSS Position Device -
Orientation by Sensors
(lat,lon)
Mapping of real world information to 2D image
Camera/Vehicle position and Orientation in Real
Time
.
.
Captured image
Captured image
Information in pixels
(x4,y4) - (lat4,lon4)
(x3,y3) - (lat3,lon3)
Known relation Depth Information
(x1,y1) - (lat1,lon1)
  • Future GIS Hibridization Capabilities
  • Precise Map Building
  • Usable information for control loops predictive

(x2,y2) - (lat2,lon2)
Measured Reference
(x0,y0) - (lat0,lon0)
15
Vision Sensor FtL results
  • Follow the Lane
  • Tx (lateral) translation in x
  • Vx linear velocity in x
  • Wx width of the lane
  • dWx linear velocity of the change of width
  • Self Assesment
  • Active Control of Light Conditions

16
Vision Sensor SLAM
  • SLAM (Simultaneous Location Mapping ) Visual
    odometry Mapping
  • Visual odometry Estimation of the EgoMotion (6D
    camera/vehicle pose) in real time
  • Real time 3D scene map generation

17
Evaluation
  • FtL Evaluation
  • Recorded video sequences 337 minutes
  • SLAM Module - 2 step evaluation
  • Laboratory computer using the KITTI odometry
    evaluation dataset with ground truth
  • 22 sequences of images recorded with a stereo
    pair of cameras embedded in a car.
  • Evaluation in San Sebastian
  • Running predifined paths

18
Evaluation
  • Accuracy and precision of the odometry
  • Translation error max 0.29
  • rotational error 0.0122 deg/m
  • Runtime 9.0 ms

19
Conclusions
  • Computer Vision as one key sensor for enabling
    autonomous driving
  • Enable autonomous or semi-autonomous driving of
    your vehicle even in situations when GNSS Signal
    is unreliable or not available at all (i.e.
    indoors, in tunnels, under dense vegetation,
    etc.).
  • Know the position of your vehicle even when no
    GNSS reception is available.
  • Improve position precision and reliability
    considerably when compared to GNSS-only solutions
  • Improve availability compared to GNSS solutions.
    SLAM is possible 24/7 while GNSS reception might
    be unreliable or not available at all for several
    minutes
  • Create a map in Real Time and Geo-locate all the
    point of an image in Real Time

20
  • THANK YOU!

Marc POLLINA pollina_at_m3systems.net
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