Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Toward Efficient and Robust Multiple Camera Visual-inertial Odometry   Efficiency and robustness are the essential criteria for the visual-inertial
odometry (VIO) system. To process massive visual data, the high cost on CPU
resources and computation latency limits VIO's possibility in integration with
other applications. Recently, the powerful embedded GPUs have great potentials
to improve the front-end image processing capability. Meanwhile, multi-camera
systems can increase the visual constraints for back-end optimization. Inspired
by these insights, we incorporate the GPU-enhanced algorithms in the field of
VIO and thus propose a new front-end with NVIDIA Vision Programming Interface
(VPI). This new front-end then enables multi-camera VIO feature association and
provides more stable back-end pose optimization. Experiments with our new
front-end on monocular datasets show the CPU resource occupation rate and
computational latency are reduced by 40.4% and 50.6% without losing accuracy
compared with the original VIO. The multi-camera system shows a higher VIO
initialization success rate and better robustness overall state estimation.

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The categories of this paper are: astro-ph
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The categories of this paper are: adap-org
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The categories of this paper are: econ
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The categories of this paper are: alg-geom
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The categories of this paper are: solv-int
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The categories of this paper are: stat
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The categories of this paper are: hep-ex
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The categories of this paper are: hep-lat
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The categories of this paper are: chao-dyn
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The categories of this paper are: gr-qc
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The categories of this paper are: dg-ga
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The categories of this paper are: ao-sci
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The categories of this paper are: cond-mat
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The categories of this paper are: eess
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The categories of this paper are: atom-ph
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The categories of this paper are: supr-con