Efficient Change Detection in 3D Environment for Autonomous ...

albeit faster. Table I uses the best times obtained in the
work of Nuñez et al. [6]. Both methods are divided into two
steps, where the first step builds a “special” representation
and the second step compares this representation (called
Detection). In the case of [6], the representation is built using
a simplification method and GMM estimation. In our method,
the representation tailored to building implicit volumes. The
change detection is achieved using structural matching in [6],
and Boolean operation in our approach.
Notice that, for all datasets, our method is substantially
faster to build the required representation. The main limitation
of the method proposed by Nuñez et al. [6] is related
to the Gaussian Mixture Models using the Expectation-
Maximization algorithm, which has lower computational performance
and is quite sensitive to the number of Gaussians,
which is one of the parameters of the EM algorithm.
Besides the improvement in computational time processing,
we empirically observe, from other results as shown
in the Figure 9, that our method performs quite robustly in
the presence of parallax and highly complex geometry. In
the work of [6], the datasets obtained by the robot show
the walls and some objects, but the roof was also acquired,
where pipes and other objects are present in the ceiling, as
shown in Figure 8-a. However, this data are not considered
in their experiment, because of to the high data complexity
due parallax and absence of meaningful changes. In another
experiment, we used the “complete” information available
from this dataset to describe the data including all the points.
Figure 9-a shows one example of “complete” point cloud
obtained using the Pioneer robot (see Fig. 8-a) where one
person produce the change (see Fig. 8-b). The blue dots
represent the points detected as not change, and the red points
as change points detected by our algorithm. Some red points
are in the ceiling, and this is due to the parallax effect in
the acquisition and misalignment between reference dataset
(without change) and the present dataset (with change).
Figure 9-b shows the clustered volume obtained with our
implicit approach for the complete dataset. The point cloud
is approximately twice as large as the partial volume, i.e. it
has approximately 150k points. We were able to correctly
detect changes for this dataset in less than one second.
V. CONCLUSIONS AND FUTURE WORK
This paper described a novel method to detect changes in
a 3D real environment for robot navigation. Results obtained
with two different experimental platforms are shown. A new
approach to detect changes based on implicit volumes allow
us to detect changes using Boolean operation. It enables us to
localize the existence of novelties in the scene, i.e. segment
them. Results of the proposed algorithm demonstrate the
reliability and efficiency of the approach. The algorithm
was compared with a state-of-art approach and obtained
substantially better results in terms of computational cost
and similar as far as detection and accuracy are concerned.
Future investigation will focus on the extension of the
method to work iteratively, with the data being captured
online by the robot. Another important improvement is to
(a) (b)
Fig. 9. Change detection algorithm comparison: a) example of point cloud
obtained using Pioneer robot (see Fig. 8-a) with one person as change (see
Fig. 8-b). Changes detected by the algorithm is indicated by red points,
where is possible to see some outliers in the roof. b) Implicit volumes
obtained from point cloud in a) generated by our approach.
perform the registration between clouds concurrently with
Boolean operation. This will ultimately allow us to correlate
and align similar points and obtain good registration even
for significant changes in environmental and acquisition
conditions.
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