automated construction of environment models by a mobile robot l.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Automated Construction of Environment Models by a Mobile Robot PowerPoint Presentation
Download Presentation
Automated Construction of Environment Models by a Mobile Robot

Loading in 2 Seconds...

play fullscreen
1 / 50

Automated Construction of Environment Models by a Mobile Robot - PowerPoint PPT Presentation


  • 267 Views
  • Uploaded on

Automated Construction of Environment Models by a Mobile Robot Thesis Proposal Paul Blaer January 5, 2005 Task: Construction of Accurate 3-D models Task: Construction of Accurate 3-D models Problem: Manual Construction

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Automated Construction of Environment Models by a Mobile Robot' - jana


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
automated construction of environment models by a mobile robot

Automated Construction of Environment Models by a Mobile Robot

Thesis Proposal

Paul Blaer

January 5, 2005

problem manual construction
Problem: Manual Construction

Even with sophisticated tools, many tasks are still accomplished manually:

  • Planning of scanning locations
  • Transportation from one scanning location to the next, possibly under adverse conditions
  • Accurately computing the exact location of the sensor
approach automate the process
Approach: Automate the Process
  • Construct a mobile platform that is capable of autonomous localization and navigation. *
  • Given a small amount of initial information about the environment, plan efficient views to model the region. *
  • Use those views to construct a photometrically and geometrically correct model.
proposed contributions
Proposed Contributions:
  • An improved 2-D view planning algorithm used for bootstrapping the construction of a complete scene model
  • A new 3-D voxel-based next-best-view algorithm
  • A topological localization algorithm combining omnidirectional vision and wireless access point signals.
  • Voronoi diagram-based path planner for navigation.
  • A model construction system that fuses the view planning algorithms with the robot’s navigation and control systems.
large scale 3 d modeling literature
Large Scale 3-D ModelingLiterature:
  • 3D City Model Construction at Berkeley – Frueh, et al, 2004, 2002
  • Outdoor Map Building at University of Tsukuba – Ohno, et al 2004
  • MIT City Scanning Project – Teller, 1997
  • Klein and Sequeira, 2004, 2000
  • Nuchter, et al, 2003
view planning literature
View Planning Literature:
  • 1. Model Based Methods
      • Cowan and Kovesi, 1988
      • Tarabanis and Tsai, 1992
      • Tarabanis, et al, 1995
      • Tarbox and Gottschlich, 1995
      • Scott, Roth and Rivest, 2001
  • 2. Non-Model Based Methods
      • Volumetric Methods
        • Connolly, 1985
        • Banta et al, 1995
        • Massios and Fisher, 1998
        • Papadopoulos-Organos, 1997
        • Soucey, et al, 1998
      • Surface-Based Methods
        • Maver and Bajcsy, 1993
        • Yuan, 1995
        • Zha, et al, 1997
        • Pito, 1999
        • Reed and Allen, 2000
        • Klein and Sequeira, 2000
      • Whaite and Ferrie, 1997
  • 3. Art Gallery Methods
      • Xie, et al, 1986
      • Gonzalez-Banos, et al, 1997
      • Danner and Kavraki, 2000
  • 4. View Planning for Mobile Robots
      • Gonzalez-Banos, et al, 2000
      • Grabowski, et al, 2003
      • Nuchter, et al, 2003
overview of our system
Overview of Our System
  • Platform
  • Steps in Our Method
    • Initial Modeling Stage
    • Planning the Robot’s Paths
    • Localization and Navigation
    • Acquiring the Scan
    • Final Modeling Stage
  • Testbeds
overview of our system the platform
Overview of Our System:The Platform

GPS

Scanner

DGPS

Autonomous Vehicle for Exploration and Navigation in Urban Environments

Network

Camera

PTU

Compass

Sonar

PC

overview of our system the method
Overview of Our SystemThe Method:
  • Initial Modeling Stage
    • Goal is to construct an initial model from which we can bootstrap construction of a complete model.
    • Compute a set of views based entirely on a known 2-D representation of the region to be modeled.
    • Compute an efficient set of paths to tour these view points
  • Final Modeling Stage
    • Voxel-based 3-D method to sequentially choose views that fill in gaps in the initial model.
initial modeling stage
Initial Modeling Stage
  • Given initial 2-D map of the scene.
  • In this stage, assume that if you see all 2-D edges of the map, you’ve seen all 3-D façades.
  • Solve the planning as a variant of the “Art Gallery” problem.
initial modeling stage13
Initial Modeling Stage
  • Problems with the “Art Gallery” approach:
    • Traditional geometric approaches assume that the guards can see 360o around with unlimited range, ignoring any constraints of the scanner.
    • A view of the 2-D footprint of an obstacle does not necessarily mean that we have seen the entire façade. There may be interesting 3-D structure above.
initial modeling stage14
Initial Modeling Stage
  • A randomized algorithm for the 2-D problem:
    • First choose a random set of potential views in the free space
initial modeling stage15
Initial Modeling Stage

100 initial samples

initial modeling stage16
Initial Modeling Stage
  • A randomized algorithm for the 2-D problem:
    • First choose a random set of potential views in the free space
    • Compute the visibility of each potential view
initial modeling stage18
Initial Modeling Stage
  • A randomized algorithm for the 2-D problem:
    • First choose a random set of potential views in the free space
    • Compute the visibility of each potential view
    • Clip the visibility of each potential view such that the constraints of our scanning system are satisfied.
initial modeling stage19
Initial Modeling Stage
  • Constraints we have added to the basic randomized algorithm:
    • Minimum and maximum range
    • Maximum grazing angle
    • Field of view
    • Overlap constraint

Scanner

Minimum Range (in our case 1m).

Maximum Range (in our case 100m).

initial modeling stage20
Initial Modeling Stage
  • Constraints we have added to the basic randomized algorithm:
    • Minimum and maximum range
    • Maximum grazing angle
    • Field of view
    • Overlap constraint

Grazing Angle

initial modeling stage21
Initial Modeling Stage
  • Constraints we have added to the basic randomized algorithm:
    • Minimum and maximum range
    • Maximum grazing angle
    • Field of view
    • Overlap constraint
initial modeling stage22
Initial Modeling Stage
  • Constraints we have added to the basic randomized algorithm:
    • Minimum and maximum range
    • Maximum grazing angle
    • Field of view
    • Overlap constraint
initial modeling stage23
Initial Modeling Stage
  • A randomized algorithm for the 2-D problem:
    • First choose a random set of potential views in the free space
    • Compute the visibility of each potential view
    • Clip the visibility of each potential view such that the constraints of our scanning system are satisfied.
    • Choose a approximate minimum subset of the potential views to cover the entire set of 2-D obstacles
initial modeling stage24
Initial Modeling Stage

9 chosen view points

initial modeling stage25
Initial Modeling Stage

A real world example:

initial modeling stage26
Initial Modeling Stage

A real world example: (1000 initial samples, 42 chosen views, 96% coverage)

planning the robot s paths
Planning the Robot’s Paths
  • Given a 2-D map of the region, compute “safe” paths for the robot to travel.
  • Keep the robot as far away from the two closest obstacles.
  • Accomplished by generating the generalized Voronoi diagram of the region and traveling along the boundaries of the Voronoi cells.
planning the robot s paths28
Planning the Robot’s Paths
  • Approximate the Generalized Voronoi Diagram:
    • Approximate the polygonal obstacles with discrete points.
    • Compute the Voronoi diagram.
    • Eliminate the edges that are inside obstacles or intersect obstacles.
planning the robot s paths30
Planning the Robot’s Paths
  • Approximate the Generalized Voronoi Diagram:
    • Approximate the polygonal obstacles with discrete points.
    • Compute the Voronoi diagram.
    • Eliminate the edges that are inside obstacles or intersect obstacles.
  • Use a shortest path algorithm such as Dijkstra’s algorithm to find paths along the Voronoi graph.
planning the robot s paths32
Planning the Robot’s Paths
  • Need to generate a tour for the robot to visit all the initially selected view points.
  • This can be treated as a “Traveling Salesman Problem” and solved with any number of approximations.
  • To generate edge weights, we first compute our “safe” Voronoi paths between all viewpoints. We use the lengths of those paths as the edge weights for our graph.
localization and navigation
Localization and Navigation
  • Existing system uses a combination of:
    • GPS
    • Odometry
    • Attitude Sensor
    • Fine grained visual localization (Georgiev and Allen, 2004)
  • Problems:
    • GPS can fail in urban canyons
    • Odometry is unreliable because of slipping and cumulative error
    • Fine grained visual localization system needs an existing position estimate
coarse localization
Coarse Localization
  • Coarse Localization System:
    • Histogram Matching with Omnidirectional Vision:
      • Fast
      • Rotationally-invariant
coarse localization36
Coarse Localization
  • Coarse Localization System:
    • Histogram Matching with Omnidirectional Vision:
      • Fast
      • Rotationally-invariant
  • Wireless signal strength of Access Points
    • Use existing wireless infrastructure to resolve ambiguities in location.
    • Look at the signal strengths to all visible base stations at a given location and compare against database.
final modeling stage
Final Modeling Stage
  • The initial modeling stage will result in an incomplete model:
    • Undetectable 3-D occlusions
    • Previously unknown obstacles
    • Temporary obstacles
  • Need a second modeling stage to fill in the holes.
final modeling stage40
Final Modeling Stage
  • We store the world as a voxel grid.
    • For view planning of large scenes the voxels do not need to be small.
    • Initial voxel grid is populated with the scans from the first stage.
      • If a voxel has a data point in it, it is marked as seen-occupied.
      • Unoccupied voxels along the straight line path from that point back to its scanning location that are marked as seen-empty.
      • All other voxels are marked as unseen.
final modeling stage41
Final Modeling Stage
  • We use the known 2-D footprints of our obstacles to mark the ground plane voxels as occupied or potential scanning locations.
final modeling stage42
Final Modeling Stage
  • For each unseen voxel that borders on an empty voxel we trace a ray back to all scanning locations.
  • If ray is not occluded by other filled voxels and it satisfies the scanner’s other constraints, that potential viewing location’s counter is incremented.
  • The potential viewing location with the largest count is chosen.
  • A new scan is taken and the process repeats until there are no unseen voxels bordering on empty voxels.
final modeling stage43
Final Modeling Stage
  • Additional Constraints:
    • Range constraint – the scanner’s minimum and maximum range is considered. If the ray is outside this range, it is not considered.
    • Overlap constraint – for each view we can also keep track of how many known voxels it can view and require a minimum overlap for registration purposes.
    • Traveling distance constraint – weight more heavily views that are closer to the current position.
    • Grazing angle constraint – this constraint is harder to implement since no surface information is stored.
final modeling stage47
Final Modeling Stage

Initial View

Next Best View

road map to the thesis
Road Map to the Thesis
  • A topological localization algorithm – implemented and tested in complicated outdoor environments (Blaer and Allen, 2002 and 2003).
  • A Voronoi-based path planner – implemented and tested (Allen et al, 2001).
  • An 2-D view planning algorithm for bootstrapping the construction of a complete model – tested on simulated and real world data. Additional constraints and testing are needed.
  • A voxel-based method for choosing next-best views – initial stages of the algorithm have been tested on simulated data.
  • Integrate these algorithms into the robot to build a complete system.