Dana Cobzas-PhD thesis Image-Based Models with Applications in Robot Navigation Dana Cobzas...
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Transcript of Dana Cobzas-PhD thesis Image-Based Models with Applications in Robot Navigation Dana Cobzas...
Dana Cobzas-PhD thesis
Image-Based Modelswith Applications inRobot Navigation
Dana CobzasSupervisor: Hong Zhang
Dana Cobzas-PhD thesis
3D Modeling in Computer Graphics
Graphics model: 3D detailed geometric model of a scene
Goal: rendering new views
New viewReal scene Geometric model + texture
RenderingAquisition
Range sensors
Modelers
[Pollefeys & van Gool]
Dana Cobzas-PhD thesis
Mapping in Mobile Robotics
Robot Map
sensors
Map Building
Localization/TrackingNavigation
environment
Navigation map: representation of the navigation space
Goal: tracking/localizing the robot
Dana Cobzas-PhD thesis
Same objective: How to model existing scenes?
Traditional geometry-based approaches:
= geometric model + surface model + light model- Modeling complex real scenes is slow - Achieving photorealism is difficult- Rendering cost is related to scene complexity+ Easy to combine with traditional graphics
Alternative approach: image-based modeling: = non-geometric model from images
- Difficult to acquire real scenes- Difficult to integrate with traditional graphics+ Achieving photo-realism is easier if starting from real photos+ Rendering cost is independent on scene complexity
In this work we combine the advantages of both for mobile robotics localization and predictive display
Dana Cobzas-PhD thesis
This thesisInvestigates the applicability of IBMR techniques in mobile robotics.
Questions addressed: Is it possible to use an IBM as navigation map for
mobile robotics? Do they provide desired accuracy for the specific
applications – localization and tracking? What advantages do they offer compared to traditional
geometric-based models?
Dana Cobzas-PhD thesis
Approach
Solution: Reconstructed geometric model combined with
image information 2 models
Model1: calibrated: panorama with depthModel2: uncalibrated: geometric model with
dynamic texture Applications in localization/tracking and
predictive display
Dana Cobzas-PhD thesis
Model1: Panoramic model
Dana Cobzas-PhD thesis
Model1: Overview
Standard panorama: - no parallax, reprojection from the same viewpoint
Solution – adding depth/disparity information:1. Using two panoramic images for stereo
2. Depth from standard planar image stereo
3. Depth from laser range-finder
Dana Cobzas-PhD thesis
Depth from stereo
Cylindrical image-based panoramic models
+ depth map
Trinocular Vision System
(Point Gray Research)
Dana Cobzas-PhD thesis
Depth from laser range-finder
180 degrees panoramic mosaic
Corresponding range data (spherical representation)
Data from different sensors: requires data registration
CCD camera Laser rangefinder Pan unit
Dana Cobzas-PhD thesis
Model 1: ApplicationsAbsolute localization:
Input: image+depthFeatures: planar patches
vertical lines
Input: intensity imageAssumes: approximate poseFeatures: vertical lines
Incremental localization:
Predictive display:
Dana Cobzas-PhD thesis
Model 2:Geometric model with dynamic
texture
Dana Cobzas-PhD thesis
Model 2: Overview
Input images Model Applications
Geometric model
Dynamic texture
Tracking
Rendering
Dana Cobzas-PhD thesis
Tracked features
Structure from motion
algorithm
poses
structure
Geometric structure
Dana Cobzas-PhD thesis
Dynamic texture
I1
tI
Input Images Re-projected geometry
Texture Variability basis
Dana Cobzas-PhD thesis
3D SSD Tracking Goal: determine camera motion (rot+transl) from image
differences Assumes: sparse geometric model of the scene Features: planar patches
past motion
currentmotion
past warp
current warpdifferential
warp
differentialmotion initial
motion
3D Model
Dana Cobzas-PhD thesis
Tracking example
Dana Cobzas-PhD thesis
Tracking and predictive display
Goal: track robot 3D pose along a trajectory Input: geometric model (acquired from images) and initial
pose Features: planar patches
Dana Cobzas-PhD thesis
Thesis contributionsContrast calibrated and uncalibrated methods for
capturing scene geometry and appearance from images:
panoramic model with depth data (calibrated)
geometric model with dynamic texture (uncalibrated)
Demonstrate the use of the models as navigation maps with applications in mobile robotics
absolute localizationincremental localizationmodel-based trackingpredictive display
Dana Cobzas-PhD thesis
Thesis questions
What advantages do they offer compared to traditional geometric based models? The image information is used to solve data association
problem. Model renderings are used for predicting robot location for a
remote user.
Do they provide desired accuracy for the specific applications – localization, tracking? The geometric model (reconstructed from images) is used
for localization/tracking algorithms. The accuracy of the algorithm depends on the accuracy of the reconstructed model.
The model accuracy can also be improved during navigation as different levels of accuracy are needed depending on the location (large space/narrow space) – future work.
Is it possible to use an image-based model as navigation map for mobile robotics? A combination of geometric and image-based model can be
used as navigation map.
Dana Cobzas-PhD thesis
Comparison with current approaches
Mobile Robotics Map+ Image information for data association+ Complete model that can be rendered – closer to human
perception- Concurrent localization and matching (SLAM-Durrant-Whyte)- Invariant features (light, occlusion) (SIFT-Lowe)- Uncertainty in feature location (localization algorithms)
Graphics Model (dynamic texture model-hybrid image+geometric model)
+ Easy acquisition: non-calibrated camera (raysets, geometric models)
+ Photorealism (geometric models)+ Traditional rendering using the geometric model (raysets)- Automatic feature detection for tracking – larger scenes- Denser geometric model (relief texture)- Light-invariance (geometric models, photogrammetry)
Dana Cobzas-PhD thesis
Future workMobile Robotics Map
Improve map during navigation Different ‘map resolutions’ depending on robot pose Incorporate uncertainty in robot pose and features Light, occlusion invariant features Predictive display: control robot’s motion by ‘pointing’ o
‘dragging’ in image space
Graphics Model (dynamic texture) Automatic feature detection for tracking Light-invariant model Compose multiple models into a scene based on intuitive
geometric constraints Detailed geometry (range information from images)