摘要
Indoor visual localization relies heavily on image retrieval to ascertain location information.However,the widespread presence and high consistency of floor patterns across different images of-ten lead to the extraction of numerous repetitive features,thereby reducing the accuracy of image retrieval.This article proposes an indoor visual localization method based on semantic segmentation and adaptive weight fusion to address the issue of ground texture interference with retrieval results.During the positioning process,an indoor semantic segmentation model is established.Semantic segmentation technology is applied to accurately delineate the ground portion of the images.Fea-ture extraction is performed on both the original database and the ground-segmented database.The vector of locally aggregated descriptors(VLAD)algorithm is then used to convert image features into a fixed-length feature representation,which improves the efficiency of image retrieval.Simul-taneously,a method for adaptive weight optimization in similarity calculation is proposed,using a-daptive weights to compute similarity for different regional features,thereby improving the accuracy of image retrieval.The experimental results indicate that this method significantly reduces ground interference and effectively utilizes ground information,thereby improving the accuracy of image retrieval.
作者
TAO Sili
QIN Danyang
YANG Jiaqiang
BIE Haoze
陶思丽;QIN Danyang;YANG Jiaqiang;BIE Haoze(College of Electronic Engineering,Heilongjiang University,Harbin 150080,P.R.China;National Mobile Communications Research Laboratory,Southeast University,Nanjing 210096,P.R.China)
基金
Supported by the National Natural Science Foundation of China(No.61971162,61771186)
the Natural Science Foundation of Heilongjiang Province(No.PL2024F025)
the Open Research Fund of National Mobile Communications Research Laboratory Southeast University(No.2023D07)
the Outstanding Youth Program of Natural Science Foundation of Heilongjiang Province(No.YQ2020F012)
the Funda-mental Scientific Research Funds of Heilongjiang Province(No.2022-KYYWF-1050).
