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November 10, 2004 Prof. Christopher Rasmussen cer@cis.udel.edu Lab web page: vision.cis.udel.edu
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Research in the DV lab Tracking, segmentation Model-building, mapping, and learning Cue combination and selection Auto-calibration of sensors Current projects: –Road following, architectural modeling
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Road Following: Background Edge-based methods: Fit curves to lane lines or road borders –[Taylor et al., 1996; Southall & Taylor, 2001; Apostoloff & Zelinsky, 2003] Region-based methods: Segment image based on discriminating charac- teristic such as color or texture –[Crisman & Thorpe, 1991; Zhang & Nagel, 1994; Rasmussen, 2002; Apostoloff & Zelinsky, 2003] from Apostoloff & Zelinsky, 2003
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Problematic Scenes for Standard Approaches No good contrast or edges, but organizing feature is vanishing point, which indicates road direction Grand Challenge sample terrainAntarctic “ice highway”
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Results: Curve Tracking Integrate vanishing point directions to get points along curves parallel to (but not necessarily on) road
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Panoramic camera v2.0a ~1.5 inches
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Correspondence-based Mosaicing Minimum of 4 corresponding points in two images sufficient to define transformation warping one into other Can be done manually or automatically
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Correspondence-based Mosaicing Translation only
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Road Shape Estimation (3 cameras) Road edge tracking –Estimate quadratic curvature via Kalman filter with Sobel edge measurements
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Motion-based Mosaicing It’s possible to make mosaics of cameras with non- overlapping fields of view provided we have sequences from them (Irani et al., 2001) –Overlapping pixels are wasted pixels We’re working on approaches for n cameras > 2
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Motivation: DARPA Grand Challenge Organized by DARPA (the U. S. Defense Advanced Research Projects Agency) A robot road race through the desert from Barstow, CA to Las Vegas, NV on March 13, 2004 Prize for the winning team: $1 million (nobody won) Running again next October with $2 million prize
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Caltech’s 2004 DGC entry “Bob”
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Problem: How to Use Roads as Cues? Bob’s track relative to course corridors (No road following) We’re working on integrating camera views from vehicle with aerial photos
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Tracing Roads in Aerial Photos
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Structure-based Obstacle Avoidance with a LADAR
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Merging Structure into Local Map Integrate raw depth measurements from several successive frames using vehicle inertial estimates Combine with camera information We’re working on calibration techniques courtesy of A. Zelinsky
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Laser-Camera Registration Range image (180 x 32) 90° horiz. x 15° vert. Video frame (360 x 240) Registered laser, camera
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3-D Building Models from Images courtesy of F. van den Heuvel Show VRML model
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Robot Platform for Mapping Project PTZ camera Wireless ethernet GPS antenna Onboard computer Analog video capture card Not shown: electronic compass, tilt sensor
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View Planning Where to take the photos from? Hard constraints: Need overlapping fields of view for stereo correspondences Soft constraints: Balance accuracy of estimated 3-D model, quality of appearance (texture maps) with acquisition, computation time –Based on camera field of view, height of building, placement of occluding objects like trees and other buildings
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Path Planning How to get a robot from point A to point B? –Criteria: Distance, difficulty, uncertainty
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Path Planning GPS-referenced CAD map of campus buildings is available Aerial photos contain information about paths, vegetation as well as buildings
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Obstacle Avoidance How to detect trash cans, people, walls, bushes, trees, etc. and smoothly combine detours around them with global path planned from map and executed with GPS?
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Segmentation-Based Path Following
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Segmentation of Road Images Using Different Cues TextureColor +T+L LaserC+T+L
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