Matching Planes using Laser and Vision.
RedVision Santander 2005
D. Ortín
J. Neira
J.M.M. Montiel
Computer Science Department.
University of Zaragoza
University of Zaragoza
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Introduction.
• Mobile robot mapping and relocation based on 2D laser
range finder
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– 2D laser segmentation on 2D segments
– Only geometrical information. Lack of non geometrical
information for recognition
– [Castellanos 98] straight segments
– [Lu 97], scan matching
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-2
0
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Geometry and texture from the images
Processing sequentially the images
[Davison 2003] monocular, [Davison 98] binocular
[Se 2002], trinocular + SIFT features
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–
–
–
–
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• Vision Based SLAM, based on point features
Proposal
• Combine vision+2D laser segments for indoor
environments
• new feature, the facet
– plane + texture
– rich non-geometrical information for recognition
» widebaseline matching
» low, 3%, false positive matching rate
– robot relative location from a single facet matching
– non-sequential observation processing
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Topological Mapping
• A facet matched between
two robot locations
produces a relative robot
location together with is
uncertainty.
• The map is codded in a
graph
– each robot location is a
node. Severeral facets are
detectec from each robot
robot location
– each arc is a facet match
between two robot
locations, hence a relative
robot location
?
(b)
(d)
(a)
(c)
(?)
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The Basic Observation.
• 2D laser scanner:
– (+) structure.
– (-) poor discrimination.
• Vision camera:
– (+) visual appearance.
– (-) just viewing directions.
– (-) viewpoint dependent.
• Robot motion parallel to the
floor, but can be a different
heights
• Facet: Joint usage of
laser+vision.
– structure
visual appearance.
University of+Zaragoza
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Recognition of Corresponding Facets
• Which facets are
correspondent?
(horizontal, different height)
• Picture vs structure:
– Variant appearance vs
invariance.
• Solve for recognition in
the original 3D space.
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Recognition of Corresponding Facets.
• Detect laser segments
(regard as vertical planes).
• Reconstruct planes →
orthoimages.
– Perspective deformation
compensation:
» foreshortening.
» scale.
• Corresponding planes
look similar.
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Recognition of Corresponding Facets.
•
Correspondence.
– Harris points
– Correlation putative
point matches
– RANSAC robust
translation fitting
•
Results
– Point matches without
spurious
– Translation between
images
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Relative Robot Location form Corresponding
Facets
• Laser scanner →
– Relative location of the
first plane.
• Orthoimages →
– Relative location of the
second plane.
• Laser scanner →
– Relative location of the
second robot.
• Relative robot location.
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Improving the point matches
• Alignment in the orthoimages:
– Non Gaussian noise.
– Orthophotos texture aliasing
artifacts.
– “Vertical” planes detection.
• Alignment in the original images:
– Guided matching using the
computed H
– Full homography fitting
– Correlation in the original images
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Improving the Camera Relative Location
• Two views of a plane.
– Point matches available
– Initial camera relative
location
– Bundle adjustment from
point matches
– full 3D relocation up to
scale factor
• 2D laser resolves for
structure ambiguities.
– Initial estimation for
relocation.
– Scale of the resolution.
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Bundle adjustment
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Facet matching results.
•
•
•
•
•
Selected 28 key nodes, that map the a corridor
Sensor at different heights
Matching 172 nodes with respect to the key 28 nodes
3% false positive arcs
13% false negative non detected arcs
absolute
percent
Total
Facet
Matches
918
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True Positive False PositiveTrue Negative False Negative
892
26
8750
1247
97%
3%
87%
13%
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Results.
Matching 3 nodes with
respect to 8 key nodes
– True positive
(it is in the map and it is
detected).
– True negative
(it is not in the map and it is
not detected).
– False positive
(it is not in the map and it is
detected as being).
– False negative
(it is in the map and it is not
detected as being).
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Results.
• False positive due to
strong symmetry
– Minor differences are
regarded as outliers.
• Possible solutions:
– Deal with multiple
matching hypothesis
– Deal with graph
cycles
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Conclusions.
• Matching facets between views
– Discriminative features.
– Relative location from only a match
– Low false positive error.
• Graph: redundant relative location hypothesis
– Multiple arcs between the same two nodes
– Graph cycles
• Future work
– Exploit the graph redundancy to detect and delete spurious arcs
– Use this technique for map building
University of Zaragoza
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• JA Castellanos, JD Tardos: Mobile Robot Localization
and Map Building: A Multisensor Fusion Approach
Dept. Inform. Eng. Syst., Univ. Zaragoza, Maria de Luna,,
1998
• F Lu, E Milios: Globally consistent range scan alignment
for environment mapping. Autonomous Robots
September 19, 1997 11:21
• A Davison: Real-Time Simultaneous Localisation and
Mapping with a Single Camera, ICCV 2003.
• A Davison and D Murray: Simultaneous Localisation
and Map-Building Using Active Vision (PDF format),
IEEE Trans. PAMI, July 2002.
• D. Ortin, J. Neira, J.M.M Montiel: “Relocation using
Laser and Vision”. IEEE Int Conf on Robotics and
Automation New Orleans, May 2004. pp1505-1510
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