A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf
.
RaP-Net: A Region-wise and Point-wise Weighting Network to Extract Robust Features for Indoor Localization
[article]
2021
arXiv
pre-print
Feature extraction plays an important role in visual localization. Unreliable features on dynamic objects or repetitive regions will interfere with feature matching and challenge indoor localization greatly. To address the problem, we propose a novel network, RaP-Net, to simultaneously predict region-wise invariability and point-wise reliability, and then extract features by considering both of them. We also introduce a new dataset, named OpenLORIS-Location, to train the proposed network. The
arXiv:2012.00234v3
fatcat:vm5bqmi3x5d55j2putisfjfdze