SYSTEMS SCIENCE & CONTROL ENGINEERING: AN OPEN ACCESS JOURNAL 221
developed in Morel and Yu (2009) to improve match-
ing accuracy. But, it is still a time consumption one.
Very recently, an oriented matching algorithm (called as
ORB Rublee, Rabaud, Konolige, & Bradski, 2011) compos-
ing features from accelerated segment test (FAST) and
rotated BRIEF feature (Rublee et al., 2011) discloses the
rapidity in detecting feature points and matching while
achieving the rough real-time performance for sparse fea-
ture point detection. For obtained match feature points in
VSLAM of mobile robots, we found that there are a large
number of false matches, which leads to the inaccuracy of
the pose estimation and the poor robustness.
It is very crucial to eliminate false matches while retain-
ing the matching quality in VSLAM. Typical approaches
include both the Fast Library for Approximate Near-
est Neighbours (FLANN) algorithm (Muja & Lowe, 2009)
and Random Sample Consensus (RANSAC) (Fischler
& Bolles, 1987) algorithm. FLANN is dependent on an arti-
ficial threshold value and RANSAC cannot accommodate
the sheer number of false matches in the set of all nearest-
neighbour matches. Recently, Bian et.al. has proposed
a grid based motion statistics (GMS) algorithm (Bian
et al., 2017) to distinguish some false and true matches.
It should be pointed out that such an algorithm only get
a rough match set by eliminating some false matches of
two images about the same 3D scene. In other words,
there still exist some false matches. As false matches do
not satisfy epipolar geometry constraints (EGCs) (Hart-
ley & Zisserman, 2003; Kushnir & Shimshoni, 2014), it
is possible to develop a new PROSAC algorithm (Chum
& Matas, 2005) by fusing an EGC model to further elim-
inate false matches. EGC, which reveals the relationship
between two views, is a basic concept in image geome-
try. Specially, suppose a point P in the three-dimensional
space can be projected into two image planes, and its
projection is denoted as p and p
in the left and right
images, respectively. This constraint shows that p
must
be located in the p corresponding polar line. In the end,
it can be utilized to get more desired quality matches for
VSLAM.
Responding the above discussion, our paper is con-
cerned with the design of novel mismatch elimination
algorithm via uniting both GMS algorithm and PROSAC
algorithm. We endeavour to deal with the following chal-
lenges: how can we introduce the concept of epipolar
geometry constraint and then utilize it to further elim-
inate mismatches, and how can we design and carry
out a series of experiments to verify the effectiveness of
proposed algorithm. To this end, first, we use the ORB
algorithm (Rublee et al., 2011) to describe feature points
and get a rough matching set with the help of hamming
distance method. Then, a new algorithm via the epipolar
geometric constraint is designed to get a large number
of high-quality matching pairs. In comparison with the
traditional ORB algorithm (Rublee et al., 2011) combined
with FLANN (Muja & Lowe, 2009) and RANSAC (Fischler
& Bolles, 1987), the proposed algorithm should possess
higher real-time performance, higher matching precision
and more number of high-quality matches. Our main
contributions can be highlighted as follows: (1) A new
epipolar geometric constraint model, combining with a
projection error function, is employed to further eliminate
mismatches; (2) an improved GMS-PROSAC algorithm,
named as GMS-EGCPROSAC algorithm, is put forward to
enhance the real-time performance and the matching
precision, and to increase the number of high-quality
matching; and (3) in comparison with the GMS, ORB, SIFT,
SURF algorithms with a standard ratio-test, the effective-
ness of proposed algorithm is adequately demonstrated
by a series of experiments.
The rest sections are organized as follows. In Section 2,
we introduce the ORB image matching algorithm includ-
ing both the ORB feature detection and the ORB feature
descriptor. In Section 3, we briefly summarize the GMS
algorithm and then put forward to an improved PROSAC
algorithm by adding an epipolar geometric constraint. Its
flow chart and the algorithm steps are provided in details.
In Section 4, we verified the validity of our algorithm
through experiments. Finally, a conclusion is drawn in
Section 5.
2. ORB image matching algorithm
In this section, we introduce the ORB image match
algorithm, which consists of oriented FAST and rotated
BRIEF feature match algorithms.
2.1. Oriented FAST
Oriented FAST is commonly obtained by adding a direc-
tion information to the well-known original FAST pro-
posed by Rosten and Drummond (2006). It should be
pointed out that FAST has neither the direction invariance
nor the scale invariance. Firstly, an image pyramid (Klein
& Murray, 2008) should be built for oriented FAST in order
to achieve the scale invariance, and then the FAST corner
is detected at each level of such a pyramid. Furthermore,
the direction of feature points can be obtained by cal-
culating the centre of mass of image blocks via the grey
matter method. In what follows, specific steps of the ori-
ented FAST are shown with the help of an example, see
Figure 1. In this figure, the right sub-figure shows a dis-
sociated Bresenham ring around the centre corner candi-
date p, where the red squares are the pixels used in the
FAST corner detection and the arc plotted by dashed line
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