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Prof. Christopher Rasmussen [email protected] Lab web page: vision.cis.udel.edu. November 10, 2004. Research in the DV lab. Tracking, segmentation Model-building, mapping, and learning Cue combination and selection Auto-calibration of sensors Current projects:

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slide1

Prof. Christopher Rasmussen

[email protected]

Lab web page:

vision.cis.udel.edu

November 10, 2004

research in the dv lab
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
road following background
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

problematic scenes for standard approaches
Problematic Scenes for Standard Approaches

Grand Challenge sample terrain

Antarctic “ice highway”

No good contrast or edges, but organizing feature is

vanishing point, which indicates road direction

results curve tracking
Results: Curve Tracking

Integrate vanishing point directions to get points along

curves parallel to (but not necessarily on) road

correspondence based mosaicing
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
road shape estimation 3 cameras
Road Shape Estimation (3 cameras)
  • Road edge tracking
    • Estimate quadratic curvature via Kalman filter with Sobel edge measurements
motion based mosaicing
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 ncameras > 2
motivation darpa grand challenge
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
problem how to use roads as cues
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

merging structure into local map
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

laser camera registration
Laser-Camera Registration

Range image (180 x 32)

90° horiz. x 15° vert.

Video frame (360 x 240)

Registered

laser, camera

3 d building models from images
3-D Building Models from Images

courtesy of F. van den Heuvel

Show VRML model

robot platform for mapping project
Robot Platform for Mapping Project

Wireless

ethernet

GPS antenna

Analog video

capture card

PTZ camera

Onboard

computer

Not shown: electronic compass, tilt sensor

view planning
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
path planning
Path Planning
  • How to get a robot from point A to point B?
    • Criteria: Distance, difficulty, uncertainty
path planning1
Path Planning

GPS-referenced CAD map of

campus buildings is available

Aerial photos contain information about

paths, vegetation as well as buildings

obstacle avoidance
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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