1 long-term image-based motion estimation dennis strelow
TRANSCRIPT
4
Problems (3)
robotic search and rescue
RhexCenter for Robot-Assisted Search and Rescue, U. of South Florida
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Problems (5)
Each of these problems requires:
six degree of freedom motion
in unknown environments
without GPS or other absolute positioning
over the long term
…and some of the problems require:
small, light, and cheap sensors
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Existing mage-based approaches (1)
Monocular image-based motion estimation is a good candidate given these requirements
In particular, simultaneous estimation of:
multiframe motion
sparse scene structure
is the most promising approach
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Existing image-based approaches (2)
Algorithms exist to estimate camera motion and sparse scene structure:
SVD-based factorization (1992)
Bundle adjustment (1950’s)
Kalman filtering (1990)
Variable state dimension filter (~1994)
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Sensitivity of motion estimation (4)
But the resulting estimates can be very sensitive to:
incorrect or insufficient image feature tracking
camera modeling and calibration errors
poor prior assumptions on the motion
poor approximations in error modeling
outlier detection thresholds
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Long-term motion estimation (1)
For applications like micro air vehicles…
…the situation is really desperate
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Long-term motion estimation (2)
Each tracked point is only visible in a small percentage of the image sequence
(Example video here of going over the wall)
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Long-term motion estimation (3)
So, we’re no longer estimating our motion with respect to a single point…
…the motion estimation essentially becomes integration, just as in odometry
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Long-term motion estimation (4)
But, harder than odometry:
odometry measurements are direct measurements of the incremental motion
whereas, as we’ve seen:
sparse image measurements can produce very poor estimates of the incremental motion
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Long-term motion estimation (5)
And since we’re essentially integrating incremental motions:
one gross error in the estimated motion finishes you
one mild qualitative error may quickly compound into a gross error, finishing you
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Long-term motion estimation (5)
And since we’re essentially integrating incremental motions:
one gross error in the estimated motion finishes you
one mild qualitative error may quickly compound into a gross error, finishing you
Even with no gross or “mild qualitative” errors the integrated motion will always drift
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Improved 6 DOF estimates (1)
Batch estimation:
uses all of the observations at once
all observations must be available before computation begins
Online estimation:
observations are incorporated as they arrive
many reasons why the filter’s prior distribution might be inaccurate
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Improved 6 DOF estimates (2)
Image measurements only
often the only option
Image and inertial measurements
can disambiguate image-only estimates
more calibration, unknowns
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Improved 6 DOF estimates (3)
Conventional images:
most common case
Omnidirectional images:
requires a more complex projection model, additional calibration
generally better motion
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Improved tracking (1)
For long-term image-based motion estimation, high-quality feature tracking is critical
A. Robustness to harsh overall motion
B. Robustness to poor image texture
C. Uniform image coverage
D. Squeeze every feature for all it’s worth
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Improved tracking (2)
A. Robustness to harsh image motion
large overall image motions
highly discontinuous 2D motion fields from nonplanar scenes
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Improved tracking (3)
B. Robustness to poor image texture
low texture
repetitive texture
one-dimensional texture
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Improved tracking (4)
C. Uniform image coverage
features should span the entire image
features that have become clumped together are redundant
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Improved tracking (5)
D. Squeeze every feature for all it’s worth
features should be tracked:
despite changes in appearance due to:
even if they are near the image boundary
distance
relative angle
specularities
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Improved tracking (7)
Leonard Smalls smalls
tracker and lone biker of the apocalypse
correlation tracker
especially hard on the little things
safe for use with the little things
mama didn’t love him not applicable
all the powers of hell at his command
maybe in 2.0
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Improved tracking (8)
Eliminates the heuristics normally used for…
handling large motions
determining when a point has:
extracting features
been mistracked
become occluded
left the image
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Improved tracking (9)
…and instead:
constrains tracking to epipolar lines
uses only 3D geometric consistency for determining when a point has:
chooses features based on image coverage
been mistracked
become occluded
left the image
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Improved tracking (10)
smalls uses…
SIFT keypoint extraction and matching
RANSAC
two-frame SFM
…to determine…
consistent matches between the images
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Improved tracking (11)
…and thence:
epipolar geometry
center of search range along epipolar lines
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Experiments (7): CMU crane
Crane capable of translating a platform…
…through x, y, z…
…through a workspace of about 10 x 10 x 5 m
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Experiments (8): CMU crane, cont.
y tr
ansl
atio
n (m
eter
s)
x translation (meters)3.00.0-3.0
-3.0
0.0
3.0
(x, y) translation ground truth
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Experiments (9): CMU crane, cont.z
(m)
time
3.5
4.5
z translation ground truth
No change in rotation
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Experiments (11): CMU crane, cont.
Hard sequence:
• Each image contains an average of 56.0 points
• Each point appears in an average of 62.3 images (4.4% of sequence)
• Image-and-inertial online algorithm applied
• 40 images used in batch initialization
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Experiments (14): CMU crane, cont.
6 DOF errors, after scaled rigid alignment:
Rotation: 0.14 radians average
Translation: 31.5 cm average (0.9% of distance traveled)
Global scale error: -3.4%
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Future work (1)
Closing the loop to deal with drift:
(1) recognizing revisited features
(2) exploiting revisited features in the estimation