Geolocalization is a crucial process that leverages environmental information and contextual data to accurately identify a position.In particular,cross-view geolocalization utilizes images from various perspectives,su...Geolocalization is a crucial process that leverages environmental information and contextual data to accurately identify a position.In particular,cross-view geolocalization utilizes images from various perspectives,such as satellite and ground-level images,which are relevant for applications like robotics navigation and autonomous navigation.In this research,we propose a methodology that integrates cross-view geolocalization estimation with a land cover semantic segmentation map.Our solution demonstrates comparable performance to state-of-the-art methods,exhibiting enhanced stability and consistency regardless of the street view location or the dataset used.Additionally,our method generates a focused discrete probability distribution that acts as a heatmap.This heatmap effectively filters out incorrect and unlikely regions,enhancing the reliability of our estimations.Code is available at https://github.com/nathanxavier/CVSegGuide.展开更多
基金financed in part by the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior-Brasil(CAPES)(88887.929508/2023-00 and 88887.937224/2024-00)partially funded by the National Research Council of Brazil(CNPq)(307525/2022-8).
文摘Geolocalization is a crucial process that leverages environmental information and contextual data to accurately identify a position.In particular,cross-view geolocalization utilizes images from various perspectives,such as satellite and ground-level images,which are relevant for applications like robotics navigation and autonomous navigation.In this research,we propose a methodology that integrates cross-view geolocalization estimation with a land cover semantic segmentation map.Our solution demonstrates comparable performance to state-of-the-art methods,exhibiting enhanced stability and consistency regardless of the street view location or the dataset used.Additionally,our method generates a focused discrete probability distribution that acts as a heatmap.This heatmap effectively filters out incorrect and unlikely regions,enhancing the reliability of our estimations.Code is available at https://github.com/nathanxavier/CVSegGuide.
