thesis by: ran zask 105221 asian institute of technology umm not sure.. where are you?
TRANSCRIPT
Thesis by: Ran Zask 105221
Asian Institute of Technology
Umm not
sure..
Where are
you?
Agenda Background
Problem statement
Related work
Our algorithm
o 3D modeling
o Texture mapping
Results
Problems & limitations
Conclusions & Recommendations
BackgroundSome robot applications require human operator (e.g.
search & rescue)
Human operator needs to maintain situation
awareness in order to control the robot.
Current means of maintaining situation awareness are
poor (2D maps, live video)
3D modeling and visualization is needed.
Problem statementReal-time SLAM implementations do not
estimate surfaces of the environment and
using expensive sensors (sonar, laser)
Structure From Motion is too heavy and done
offline.
Real-time 3D modeling and visualization is
feasible and needed.
Related workPollefeys et al. (2004) : point-based modeling
o Monocular camera
o 3D reconstruction from feature points
o Bundle adjustment
o Rectification, Dense stereo
Offline algorithm, Massive computation
Similar application: Microsoft Photosynth
Related work (cont.)Johnson and Kang (1999) : grid-based
modeling
o Omni-directional stereo
o 2D Delauney triangulation
o Iterative closest point (ICP)
oMesh registration
Massive computation
The algorithmSingle camera, calibratedRequires SLAM (point-based)Uses 3D points and occupancy gridsOnline algorithmCreates 3D models with texture mappingProvides low metric accuracyLess noise between grid cells
The algorithmUses 2 occupancy grids:Local grid
o Recreated each frame
o Contains ‘fullness’ seen at the current frame
o Incrementally added to the global grid.Global grid
o Contains the incremental ‘fullness’
o Used for Iso-surface calculation
The algorithm (3D modeling)For each image: Input: new 3D point set (from SLAM)Reset local gridAssociate 3D points with cells in the local gridIgnore cells associated by few pointsAdd camera-center as an occupied cellFill-in the grid (convex-hull)Merge local grid into global gridIso-surface the global grid -> last model
The algorithm (texture mapping)For each image : (After having the latest model)Classify triangles as: “new,” “old,” and
“expired”Project “new” triangles onto current frame ->
textmapsRemove “expired” triangles
ResultsModeling accuracy is satisfyingTexture mapping
Tessellation within a patch: perfect
Tessellation between patches: Inaccurate
The more images are closer to each other, the better the modeling and the texture mapping
“patch” – collection of several triangles of the same image
Modeling results
Modeling results
Modeling results
SummaryWe created an online algorithm for modeling
with texture, all from a single camera.
Low computation is required per image
This should allow short baseline -> good
results
Real time implementation of the system is
feasible.
Future workCreate a real-time system
Create 3D tools for the human operator to
easily ‘walk’ in the model and navigate the
robot.
Improving the algorithm even more:
performing iso-surface on tighter grids
