Title:
Visual Odometry Priors for robust EKF-SLAM
Visual Odometry Priors for robust EKF-SLAM
Author(s)
Alcantarilla, Pablo F.
Bergasa, Luis Miguel
Dellaert, Frank
Bergasa, Luis Miguel
Dellaert, Frank
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Abstract
One of the main drawbacks of standard
visual EKF-SLAM techniques is the assumption of a
general camera motion model. Usually this motion
model has been implemented in the literature as a
constant linear and angular velocity model. Because
of this, most approaches cannot deal with sudden
camera movements, causing them to lose accurate
camera pose and leading to a corrupted 3D scene map.
In this work we propose increasing the robustness
of EKF-SLAM techniques by replacing this general
motion model with a visual odometry prior, which
provides a real-time relative pose prior by tracking
many hundreds of features from frame to frame.
We perform fast pose estimation using the two-stage
RANSAC-based approach from [1]: a two-point algorithm for rotation followed by a one-point algorithm
for translation. Then we integrate the estimated relative pose into the prediction step of the EKF. In
the measurement update step, we only incorporate a
much smaller number of landmarks into the 3D map to
maintain real-time operation. Incorporating the visual
odometry prior in the EKF process yields better and
more robust localization and mapping results when
compared to the constant linear and angular velocity
model case. Our experimental results, using a stereo
camera carried in hand as the only sensor, clearly
show the benefits of our method against the standard
constant velocity model.
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Date Issued
2010
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