(Georgia Institute of Technology, 2010)
Alcantarilla, Pablo F.; Oh, Sang Min; Mariottini, Gian Luca; Bergasa, Luis M.; Dellaert, Frank
We aim to perform robust and fast
vision-based localization using a pre-existing large
map of the scene. A key step in localization is associating the features extracted from the image with
the map elements at the current location. Although
the problem of data association has greatly benefited
from recent advances in appearance-based matching
methods, less attention has been paid to the effective
use of the geometric relations between the 3D map
and the camera in the matching process.
In this paper we propose to exploit the geometric
relationship between the 3D map and the camera
pose to determine the visibility of the features. In
our approach, we model the visibility of every map
feature w.r.t. the camera pose using a non-parametric
distribution model. We learn these non-parametric
distributions during the 3D reconstruction process,
and develop efficient algorithms to predict the visibility of features during localization. With this approach,
the matching process only uses those map features
with the highest visibility score, yielding a much
faster algorithm and superior localization results. We
demonstrate an integrated system based on the proposed idea and highlight its potential benefits for the
localization in large and cluttered environments.
(Georgia Institute of Technology, 2010)
Alcantarilla, Pablo F.; Bergasa, Luis Miguel; Dellaert, Frank
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.