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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

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

- 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:

- 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 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:

- 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

- 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

GPS

Scanner

DGPS

Autonomous Vehicle for Exploration and Navigation in Urban Environments

Network

Camera

PTU

Compass

Sonar

PC

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

- 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 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 Stage

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

Initial Modeling Stage

100 initial samples

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 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 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 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 Stage

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

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 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 Stage

9 chosen view points

Initial Modeling Stage

A real world example:

Initial Modeling Stage

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

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 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 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 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

- 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 System:
- Histogram Matching with Omnidirectional Vision:
- Fast
- Rotationally-invariant

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

- 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 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 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 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 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.

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.

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