Prof. Christopher Rasmussen
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Prof. Christopher Rasmussen [email protected] Lab web page: 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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November 10, 2004

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November 10 2004

Prof. Christopher Rasmussen

[email protected]

Lab web page:

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

Panoramic camera v2 0a

~1.5 inches

Panoramic camera v2.0a

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

Correspondence based mosaicing1

Correspondence-based Mosaicing

Translation only

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

Caltech s 2004 dgc entry bob

Caltech’s 2004 DGC entry “Bob”

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

Tracing roads in aerial photos

Tracing Roads in Aerial Photos

Structure based obstacle avoidance with a ladar

Structure-based Obstacle Avoidance with a LADAR

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)


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



GPS antenna

Analog video

capture card

PTZ camera



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?

Segmentation based path following

Segmentation-Based Path Following

Segmentation of road images using different cues

Segmentation of Road Images Using Different Cues


Color +T+L



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