Place　recognition　is　a　cornerstone　of　vehicle　navigation　and　mapping,　which　is　pivotal　in　enabling　systems　to　determine　whether　a　location　has　been　previously　visited.　This　capability　is　critical　for　tasks　such　as　loop　closure　in　Simultaneous　Localization　and　Mapping　(SLAM)　and　long-term　navigation　under　varying　environmental　conditions.　This　survey　comprehensively　reviews　recent　advancements　in　place　recognition,　emphasizing　three　representative　methodological　paradigms:　Convolutional　Neural　Network　(CNN)-based　approaches,　Transformer-based　frameworks,　and　cross-modal　strategies.　We　begin　by　elucidating　the　significance　of　place　recognition　within　the　broader　context　of　autonomous　systems.　Subsequently,　we　trace　the　evolution　of　CNN-based　methods,　highlighting　their　contributions　to　robust　visual　descriptor　learning　and　scalability　in　large-scale　environments.　We　then　examine　the　emerging　class　of　Transformer-based　models,　which　leverage　self-attention　mechanisms　to　capture　global　dependencies　and　offer　improved　generalization　across　diverse　scenes.　Furthermore,　we　discuss　cross-modal　approaches　that　integrate　heterogeneous　data　sources　such　as　Lidar,　vision,　and　text　description,　thereby　enhancing　resilience　to　viewpoint,　illumination,　and　seasonal　variations.　We　also　summarize　standard　datasets　and　evaluation　metrics　widely　adopted　in　the　literature.　To　the　best　of　our　knowledge,　no　prior　survey　has　systematically　reviewed　visual,　LiDAR,　and　cross-modal　place　recognition　concurrently.　This　work　thus　resolves　a　critical　gap　in　existing　literature　dominated　by　single-modality　studies.　Finally,　we　identify　current　research　challenges　and　outline　prospective　directions,　including　domain　adaptation,　real-time　performance,　and　lifelong　learning,　to　inspire　future　advancements　in　this　domain.　The　unified　framework　of　leading-edge　place　recognition　methods,　i.e.,　code　library,　and　the　results　of　their　experimental　evaluations　are　available　at　https://github.com/CV4RA/SOTA-Place-Recognitioner.　©　The　Author(s)　2025.