 Experimental results show that the proposed RaP-Net trained with the OpenLORIS-Location dataset significantly outperforms existing CNN-based keypoint extraction algorithms for indoor localization.
<meaning-changed>  Experimental results show that the proposed RaP-Net trained with the OpenLORIS-Location dataset significantly outperforms existing CNN-based keypoint extraction algorithms for indoor localization.
 Experimental results show that the proposed RaP-Net trained with the OpenLORIS-Location dataset achieves an excellent performance in the feature matching task and significantly outperforms state-of-the-arts feature algorithms in indoor localization.