Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Scalable FPGA-based Architecture for Depth Estimation in SLAM   The current state of the art of Simultaneous Localisation and Mapping, or
SLAM, on low power embedded systems is about sparse localisation and mapping
with low resolution results in the name of efficiency. Meanwhile, research in
this field has provided many advances for information rich processing and
semantic understanding, combined with high computational requirements for
real-time processing. This work provides a solution to bridging this gap, in
the form of a scalable SLAM-specific architecture for depth estimation for
direct semi-dense SLAM. Targeting an off-the-shelf FPGA-SoC this accelerator
architecture achieves a rate of more than 60 mapped frames/sec at a resolution
of 640x480 achieving performance on par to a highly-optimised parallel
implementation on a high-end desktop CPU with an order of magnitude improved
power consumption. Furthermore, the developed architecture is combined with our
previous work for the task of tracking, to form the first complete accelerator
for semi-dense SLAM on FPGAs, establishing the state of the art in the area of
embedded low-power systems.

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The categories of this paper are: q-bio
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The categories of this paper are: cmp-lg
19
The categories of this paper are: eess
24
The categories of this paper are: dg-ga
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The categories of this paper are: econ
0
The categories of this paper are: hep-ph
27
The categories of this paper are: comp-gas
32
The categories of this paper are: atom-ph
21
The categories of this paper are: adap-org
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The categories of this paper are: quant-ph
17
The categories of this paper are: q-fin
31
The categories of this paper are: ao-sci
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The categories of this paper are: nlin
34
The categories of this paper are: chem-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: math