Automated Construction of Environment Models by a Mobile Robot

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Automated Construction of Environment Models by a
Mobile Robot

• Thesis Proposal
• Paul Blaer
• January 5, 2005

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Task Construction of Accurate 3-D
models
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Task Construction of Accurate 3-D
models
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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

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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.

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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 robots navigation
  and control systems.

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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

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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

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Overview of Our System

• Platform
• Steps in Our Method
• Initial Modeling Stage
• Planning the Robots Paths
• Localization and Navigation
• Acquiring the Scan
• Final Modeling Stage
• Testbeds

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Overview of Our SystemThe Platform
GPS
Scanner
DGPS
Autonomous Vehicle for Exploration and Navigation
in Urban Environments
Network
Camera
PTU
Compass
Sonar
PC
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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.

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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, youve seen all 3-D façades.
• Solve the planning as a variant of the Art
  Gallery problem.

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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.

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Initial Modeling Stage

• A randomized algorithm for the 2-D problem
• First choose a random set of potential views in
  the free space

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Initial Modeling Stage
100 initial samples
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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

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Initial Modeling Stage
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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.

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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).
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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
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Initial Modeling Stage

• Constraints we have added to the basic randomized
  algorithm
• Minimum and maximum range
• Maximum grazing angle
• Field of view
• Overlap constraint

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Initial Modeling Stage

• Constraints we have added to the basic randomized
  algorithm
• Minimum and maximum range
• Maximum grazing angle
• Field of view
• Overlap constraint

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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

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Initial Modeling Stage
9 chosen view points
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Initial Modeling Stage
A real world example
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Initial Modeling Stage
A real world example (1000 initial samples, 42
chosen views, 96 coverage)
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Planning the Robots 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.

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Planning the Robots 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.

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Planning the Robots Paths
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Planning the Robots 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 Dijkstras
  algorithm to find paths along the Voronoi graph.

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Planning the Robots Paths
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Planning the Robots 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.

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Planning the Robots Paths
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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

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Coarse Localization

• Coarse Localization System
• Histogram Matching with Omnidirectional Vision
• Fast
• Rotationally-invariant

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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.

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Acquiring the Scan
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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.

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Final Modeling Scan
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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.

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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.

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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 scanners other constraints,
  that potential viewing locations 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.

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Final Modeling Stage

• Additional Constraints
• Range constraint the scanners 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.

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Final Modeling Stage
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Final Modeling Stage
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Final Modeling Stage
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Final Modeling Stage
Initial View
Next Best View
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Testbeds Columbia Campus
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Testbeds Governors Island
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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.