Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Integrating Deep-Learning-Based Image Completion and Motion Planning to
  Expedite Indoor Mapping   The challenge of autonomous indoor mapping is addressed. The goal is to
minimize the time required to achieve a predefined percentage of coverage with
some desired level of certainty. The use of a pre-trained generative deep
neural network, acting as a map predictor, in both the motion planning and the
map construction is proposed in order to expedite the mapping process. The
issue of planning under partial observability is tackled by maintaining a
belief map of the floorplan, generated by a deep neural network. This allows
the agent to shorten the mapping duration, as well as enabling it to make
better-informed decisions. This method is examined in combination with several
motion planners for two distinct floorplan datasets. Simulations are run for
several configurations of the integrated map predictor, the results of which
reveal that by utilizing the prediction a significant reduction in mapping time
is possible. When the prediction is integrated in both motion planning and map
construction processes it is shown that the mapping time may in some cases be
cut by over 50%.

32
The categories of this paper are: atom-ph
16
The categories of this paper are: stat
20
The categories of this paper are: acc-phys
17
The categories of this paper are: q-fin
23
The categories of this paper are: solv-int
3
The categories of this paper are: physics
5
The categories of this paper are: gr-qc
34
The categories of this paper are: chem-ph
11
The categories of this paper are: hep-lat
2
The categories of this paper are: math
15
The categories of this paper are: nucl-ex
25
The categories of this paper are: patt-sol
13
The categories of this paper are: quant-ph
7
The categories of this paper are: hep-th
8
The categories of this paper are: hep-ex
26
The categories of this paper are: bayes-an