Automated Construction of Environment Models by a Mobile Robot

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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.**Final Modeling Stage**Initial View Next Best View**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.