This　comprehensive　review　paper　explores　the　state　of　the　art　in　visual　localization　and　navigation,　drawing　on　the　principles　and　methodologies　of　three　significant　deep　learning　networks:　Visual　Localization　Network　(VLocNet),　Deep　Fusion　Network　(DFNet),　and　Hybrid　Frontend　Network　(HFNet).　Each　of　these　networks　demonstrates　the　application　of　deep　learning　to　spatial　awareness　and　navigation　tasks　in　unique　and　significant　ways.　Rather　than　an　exhaustive　dissection　of　these　networks,　the　paper　provides　an　encompassing　overview,　illuminating　their　underlying　principles,　architectural　design,　and　their　relative　performance　within　the　field.　Additionally,　the　paper　delves　into　the　practical　implications　of　these　networks,　examining　their　applications　in　diverse　real-world　scenarios.　It　underlines　this　examination　with　a　comprehensive　analysis　of　existing　literature　and　experimental　results,　intended　to　impart　a　profound　understanding　of　these　networks＇　strengths,　limitations,　and　potential　application　areas.　Ultimately,　this　review　aims　to　present　a　valuable　compass　to　researchers　navigating　the　evolving　landscape　of　advancements　in　visual　localization　and　navigation,　thereby　fostering　enriched　understanding　and　facilitating　future　exploration　and　development　in　this　compelling　field.　©　2023　IEEE.