Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Enhancing Multi-Robot Perception via Learned Data Association   In this paper, we address the multi-robot collaborative perception problem,
specifically in the context of multi-view infilling for distributed semantic
segmentation. This setting entails several real-world challenges, especially
those relating to unregistered multi-agent image data. Solutions must
effectively leverage multiple, non-static, and intermittently-overlapping RGB
perspectives. To this end, we propose the Multi-Agent Infilling Network: an
extensible neural architecture that can be deployed (in a distributed manner)
to each agent in a robotic swarm. Specifically, each robot is in charge of
locally encoding and decoding visual information, and an extensible neural
mechanism allows for an uncertainty-aware and context-based exchange of
intermediate features. We demonstrate improved performance on a realistic
multi-robot AirSim dataset.

7
The categories of this paper are: hep-th
10
The categories of this paper are: q-bio
29
The categories of this paper are: funct-an
12
The categories of this paper are: nucl-th
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The categories of this paper are: mtrl-th
25
The categories of this paper are: patt-sol
24
The categories of this paper are: dg-ga
19
The categories of this paper are: eess
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The categories of this paper are: cmp-lg
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The categories of this paper are: adap-org
15
The categories of this paper are: nucl-ex
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The categories of this paper are: chao-dyn
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The categories of this paper are: math-ph
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The categories of this paper are: nlin
11
The categories of this paper are: hep-lat
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The categories of this paper are: bayes-an