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A Distributed Processing Architecture for Vision Based Domestic Robot Navigation

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A Distributed Processing Architecture for Vision Based Domestic Robot Navigation Marcel-Titus Marginean and Chao Lu Computer & Information Sciences – PowerPoint PPT presentation

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Title: A Distributed Processing Architecture for Vision Based Domestic Robot Navigation


1
A Distributed Processing Architecture for Vision
Based Domestic Robot Navigation
  • Marcel-Titus Marginean and Chao Lu
  • Computer Information Sciences
  • Towson University

2
A Distributed Processing Architecture for Vision
Based Domestic Robot Navigation
  • Highlights
  • Distributed architecture for indoor robot
    navigation
  • On board and external computer vision
  • Communication protocol for cooperative
    localization and mapping
  • Distributed processing and decision making

3
A Distributed Processing Architecture for Vision
Based Domestic Robot Navigation
  • Rationale
  • Aging population grows, and they require
    assistance
  • Robots can help with domestic tasks
  • Enable Independent Living instead of
    Institutionalization
  • Allows the aging population to live in their own
    homes while monitoring the health status and
    providing assistance

4
A Distributed Processing Architecture for Vision
Based Domestic Robot Navigation
  • Rationale-Cont.
  • Houses already have networks and surveillance
    cams
  • Vision processing is very CPU/Memory intensive
  • Energy efficient embedded computers on robots
    still low in resources
  • Redundancies provides fault / error tolerance

5
A Distributed Processing Architecture for Vision
Based Domestic Robot Navigation
  • Rationale-Cont.
  • Computer Vision is most promising technology in
    robot navigation
  • We employ helper technologies to ease the load
  • Innate / priory knowledge about the environment
    should be used to reduce the scope of the problem

6
A Distributed Processing Architecture for Vision
Based Domestic Robot Navigation
  • Previous work
  • Mehdi et al. helped navigation with ultrasonic
    sensors and RFID tags
  • Souza and Gonclaves used stereo vision for
    mapping
  • Fernandez et al. placed artificial landmarks on
    the ceiling and used a vertical looking camera on
    the robot
  • At Cluj-Napoca a laser beam has been used to
    detect dynamic obstacles

7
A Distributed Processing Architecture for Vision
Based Domestic Robot Navigation
  • Previous work-Cont.
  • Pizaro et al. used a rig of calibrated and
    synchronized cameras
  • Chakravarty, Punarjay, Jarvis and Ray also helped
    the mobile robot navigation with external cameras

8
A Distributed Processing Architecture for Vision
Based Domestic Robot Navigation
Overview Base Station One or more general
purpose
computers Robot Embedded System
Camera Inertial Unit Network Typical House
WiFi Wired Network Fixed
Cameras IP cameras
wired or WiFi Engineering console Laptop used
for development and testing

9
A Distributed Processing Architecture for Vision
Based Domestic Robot Navigation
  • Epipolar Geometry
  • Two or more cameras oversee the same scene from
    two different points and orientations
  • The projections of a point in space on the two
    image planes are related by an equation involving
    the Essential Matrix
  • 8 pairs of matching points are to be identified
    in order to be able to calculate the Essential
    Matrix
  • Pose and relative position of cameras can be
    calculated from Essential Matrix using (SVD)
    Singular Value Decomposition method

10
A Distributed Processing Architecture for Vision
Based Domestic Robot Navigation
  • Epipolar Geometry
  • One camera can be the camera on the robot and the
    other the camera mounted on the wall
  • ?Can be used to either calculate the robot
    position in respect to fixed camera or to
    accurately map objects in the environment
  • Susceptible to failures if difference in pose /
    position is too large or if similar patterns
    located in different places are encountered

11
A Distributed Processing Architecture for Vision
Based Domestic Robot Navigation
  • Object Tracking
  • Fixed camera oversee the scene and can map the
    movement of the robot
  • A Gaussian Mixture Model is used for background
    subtraction in order to detect the moving or
    moved objects in respect to fixed background
  • Each moving object is defined by a status vector
    containing id, position, velocity and the
    confidence in the measurement.

12
A Distributed Processing Architecture for Vision
Based Domestic Robot Navigation
  • Optical Flow Navigation
  • Used by on-board (robot) computer to detect
    potential collisions and for reactive navigation
    when outside of the view of fixed cameras.
  • Optical flow field is a velocity field
    representing the projection on the image plane of
    the motion of objects in 3D space.
  • Can be used to calculate the distance from the
    moving robot to the obstacles in front or to
    maintain distance from walls when navigating into
    a hallway.

13
A Distributed Processing Architecture for Vision
Based Domestic Robot Navigation
  • Fixed Infrastructure
  • Wall mounted DCS-930L and DCS-932L IP cameras
    located near ceiling overlooking the room
  • Typical house network with 802.11n Wi-Fi router
    having 10/100 wired Ethernet
  • A pair of computers running Mageia Linux
    connected to the router with wired Ethernet.
    Called Base Station they are used for for video
    processing, model/map building, object tracking
    and mission planing
  • Object recognition for future research and will
    also take place on the Base Station

14
A Distributed Processing Architecture for Vision
Based Domestic Robot Navigation
  • Mobile Infrastructure
  • Mobile platform having Ackerman Steering
  • BeagleBoard-XM embedded computer running Debian
    ARM Linux connected to network with USB Wi-Fi
    dongle
  • LI-5M03 Camera Board connected directly to
    BeagleBoard bus
  • Inertial measurement unit with ADXL345
    accelerometer and L3G4200 MEMS gyroscope
  • Additional circuitry
  • For future research we have in the plan to
    explore the Adaptevas Paralella board to add
    extra processing

15
A Distributed Processing Architecture for Vision
Based Domestic Robot Navigation
Video Capture Hardware
Wall mounted DCS-932L WiFi IP Camera
BeagleBoard-XM with LI-5M03 Camera on test bench
16
A Distributed Processing Architecture for Vision
Based Domestic Robot Navigation
  • Software Development Environment
  • OpenCV 2.4.5 for image processing
  • QT 4.8.5 and OpenGL libs for GUI development
  • C programming language, gcc version 2.7.2
  • Eclipse CDT and QT-Creator as IDE
  • Mageia Linux Desktop

17
A Distributed Processing Architecture for Vision
Based Domestic Robot Navigation
  • Software Architecture
  • Modular Architecture using Active Objects
  • Message Passing Asynchronous Protocol
  • Messages structure designed to minimize the use
    of network bandwidth
  • Most of image processing localized on each
    module. Large data sent between modules only AS
    NEEDED upon request
  • Communication Infrastructure API abstracts the
    location of modules
  • Each Module is an Active Object with at least two
    threads (communication, main processing)

18
A Distributed Processing Architecture for Vision
Based Domestic Robot Navigation
Software Modules CM Camera Module SAM
Situation Awareness Module RM Robot
Module ARM Autonomous Robot Module
19
A Distributed Processing Architecture for Vision
Based Domestic Robot Navigation
  • Camera Module
  • One CM for each fixed camera. Capture,
    pre-processing, blob tracking
  • Sending periodic Blob Tracking Vectors to SAM
  • Upon request, send whole images or sub-images for
    analysis by other modules

20
A Distributed Processing Architecture for Vision
Based Domestic Robot Navigation
  • Situation Awareness Module
  • Maintain a live map of the environment, keeping
    track of people, objects and robots.
  • Receive periodic tracking vectors from CMs and
    RMs and match blobs with robots and provide robot
    tracking info
  • Future developments may include object
    recognition and maintaining a database for
    recognition task

21
A Distributed Processing Architecture for Vision
Based Domestic Robot Navigation
  • Robot Module
  • Robot path planning and mission control
  • Uses epipolar geometry to map objects or robot
    pose by requesting images from both ARM and CM
  • Translate tracking information from SAM global
    coordinate system into robots local coordinate
    system
  • Future research direction may include landmark
    tracking

22
A Distributed Processing Architecture for Vision
Based Domestic Robot Navigation
  • Autonomous Robot Module
  • Optical Flow (OF) processing and reactive OF
    navigation
  • PID controller to maintain a required trajectory
  • Honors request from RM for (sub) images
  • Able to temporary overrule RM commands if optical
    flow detect high potential for collision
  • Future research direction may include more
    processing power to enable true autonomy, more
    sensors and actuators for eye-hand coordination

23
A Distributed Processing Architecture for Vision
Based Domestic Robot Navigation
  • Typical navigation scenario
  • RM interrogates SAM for a map
  • RM uses Dijkstra algorithm to find the path
  • RM downloads navigation instructions into ARM
  • CMs keeps broadcasting blob position to SAM
  • SAM provides real-time tracking information to RM
  • ARM uses a PID controller to navigate on path
    using tracking info from SAM as feedback

24
A Distributed Processing Architecture for Vision
Based Domestic Robot Navigation
  • Typical navigation scenario
  • Robot encounter obstacles unknown to SAM
  • Optical flow on ARM detects it as an obstacle
  • ARM sends obstacle info to RM
  • RM requests image from a CM and from ARM
  • RM maps the object using epipolar geometry
  • Send information to SAM to update the occupancy
    grid
  • Navigation re-starts with new path planning

25
A Distributed Processing Architecture for Vision
Based Domestic Robot Navigation
  • Ideas for future direction in research
  • Explore landmark based navigation and object
    recognition
  • Increase the processing power on mobile unit by
    using something like Adaptevas Paralella board
  • The ability to create a Visual Aspect Indexed
    Database for object recognition from a large
    subset of classes of objects
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