Transcript Dense Visual Odometry - Computer Vision Group

Real-time Dense Visual Odometry
for Quadrocopters
Christian Kerl
11.05.2012
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Outline
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Motivation
Hardware & Software
Approach
Problems
Ideas
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Motivation
• Quadrocopters need sensors to fly in unknown
environments
– Motion
– Position
– Obstacles
• Restricted on-board sensors
– IMU
– Visual navigation (no GPS)
• Restricted computing resources
 Autonomous system
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Motivation
• Standard approach to visual odometry:
– Sparse feature tracking in intensity / color images
– Examples: Jakob, ETH Zurich, TU Graz, MIT
– On-board frame rates 10 Hz
• Our approach:
– Using full RGB-D image information
– No feature tracking
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Hardware – Asctec Pelican
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Hardware – Asctec Pelican
IMU
AutoPilot
Board
Atom
Board
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Hardware – Asctec Pelican
• IMU
– 3 axis magnetometer, gyroscope, accelerometer
• AutoPilot Board
– Highlevel + Lowlevel Processor (ARM)
• Atom Board
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Intel Atom Z530 1.6 GHz
1 GB RAM
7 Mini-USB Ports
WirelessLAN
• 600 g payload
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Software – Asctec Pelican
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ROS drivers for Asctec Pelican from ETH Zurich
Nonlinear dynamic inversion for position control
Luenberger Observer for data fusion
Updated version using Extended Kalman Filter to
be presented on ICRA 2012
• Needs absolute position input from external
source
• Allows to command accelerations, velocities or
positions
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Hardware – Asus Xtion Pro Live
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24 bit RGB image
16 bit depth image
640x480 @ 30 Hz
150 g
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On-camera RGB and depth image registration
Time synchronized depth and RGB image
Rolling shutter
Auto exposure
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Approach
• Estimate transformation minimizing squared
intensity error (energy minimization)
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Approach
• Linearization
• with
• minimize
=> solve normal equations
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Analysis
• Estimate transformation minimizing squared
intensity error (energy minimization)
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X translation
Y translation
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Image Data from Quadrocopter
• Hovering
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Image Data from Quadrocopter
• Trajectory along camera z-axis
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Problems
• Motion blur
• Auto exposure
• Dynamic objects (humans)
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Problems – Motion Blur
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Problems – Motion Blur
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Problems – Motion Blur
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Problems – Motion Blur
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Problems – Motion Blur
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Problems – Motion Blur
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Problems – Motion Blur
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Problems – Auto Exposure
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Problems – Auto Exposure
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Problems – Dynamic Objects
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Ideas
• Weighted Least Squares
• Initial motion estimate between 2 consecutive
frames from IMU data fusion
• Multiple iterations per level, convergence
checks
• Regularization term to minimize / constrain
least squares solution
• Minimization of intensity and depth error
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Ideas – Weighted Least Squares
• Assign smaller weight to residual outliers
=>
• Weight calculation
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Ideas – Weighted Least Squares
• Influence function
– Tukey weight
– Huber weight
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Ideas – Weighted Least Squares
• Weighted error
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Ideas – Weighted Least Squares
• Influence on energy function
X translation w/o weights
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X translation w/ Huber weights
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Ideas – Weighted Least Squares
• Influence on energy function
Y translation w/o weights
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Y translation w/ Huber weights
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Ideas – Weighted Least Squares
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Ideas – Weighted Least Squares
• Robustification with respect to dynamic
objects
• Slightly degrades tracking performance
• How to choose parameter b?
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Ideas – Initialization from IMU
• Use transformation from IMU data fusion as
initial estimate
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Ideas – Initialization from IMU
• Use transformation from IMU data fusion as
initial estimate
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Ideas – Initialization from IMU
• Use transformation from IMU data fusion as
initial estimate
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Ideas – Multiple Iterations
• Perform multiple optimization steps per image
pyramid level
• Stop when increment below threshold
• Bad frames / diverging
results can be recognized
and skipped
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Summary/Discussion
• Weighted Least Squares needs more work
(especially weight calculation)
• Initialization from IMU promising
• Multiple Iterations for increased accuracy and
divergence detection promising, but
computationally expensive
• Jumps in trajectory are really problematic!
=> Ideas welcome!
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