Person:
Dellaert,
Frank
Publication Search Results
GroupSAC: Efficient Consensus in the Presence of Groupings
Flow Separation for Fast and Robust Stereo Odometry
Person:
Dellaert,
Frank
1 - 2 of 2
Item
We present a novel variant of the RANSAC algorithm
that is much more efficient, in particular when dealing with
problems with low inlier ratios. Our algorithm assumes
that there exists some grouping in the data, based on which
we introduce a new binomial mixture model rather than
the simple binomial model as used in RANSAC. We prove
that in the new model it is more efficient to sample data
from a smaller numbers of groups and groups with more
tentative correspondences, which leads to a new sampling
procedure that uses progressive numbers of groups. We
demonstrate our algorithm on two classical geometric vision
problems: wide-baseline matching and camera resectioning.
The experiments show that the algorithm serves
as a general framework that works well with three possible
grouping strategies investigated in this paper, including
a novel optical flow based clustering approach. The
results show that our algorithm is able to achieve a significant
performance gain compared to the standard RANSAC
and PROSAC.
Item
Separating sparse flow provides fast and robust
stereo visual odometry that deals with nearly degenerate
situations that often arise in practical applications.We make use
of the fact that in outdoor situations different constraints are
provided by close and far structure, where the notion of close
depends on the vehicle speed. The motion of distant features
determines the rotational component that we recover with a
robust two-point algorithm. Once the rotation is known, we
recover the translational component from close features using
a robust one-point algorithm. The overall algorithm is faster
than estimating the motion in one step by a standard RANSAC-based
three-point algorithm. And in contrast to other visual
odometry work, we avoid the problem of nearly degenerate
data, under which RANSAC is known to return inconsistent
results. We confirm our claims on data from an outdoor robot
equipped with a stereo rig.