Recent　years　have　witnessed　a　fast　evolution　and　promising　performance　of　the　vision　transformer　(ViT)-based　place　recognizer,　which　aims　at　building　a　general　system.　State-of-the-arts　(SOTAs)　can　hardly　carry　on　their　superiority　at　low　light　so　far,　thereby　considerably　blocking　the　broadening　of　visual　place　recognition-related　mobile　robot　applications.　To　perform　robust　visual　place　recognition　in　low-light　scenes,　this　article　proposes　an　end-to-end　trainable　dark-enhanced　Net,　which　tries　to　alleviate　the　impact　of　poor　illumination　and　environmental　noise.　Specifically,　a　lightweight　dark　enhancement　module,　i.e.,　sf　ResEM,　is　firstly　trained　to　efficiently　improve　image　illumination　quality　by　residual-based　adversarial　learning.　A　dual-level　sampling　pyramid　transformer,　i.e.,　sf　DSPFormer,　is　then　constructed　to　extract　discriminative　features　through　aggregating　reconstructed　descriptors.　Moreover,　to　improve　the　performance　and　reliability　of　place　recognition,　a　reranking　method　based　on　cross-entropy　loss　is　used　for　final　place　matching.　To　provide　a　comprehensive　evaluation,　we　also　build　two　challenging　place　benchmarks,　namely,　sf　SimPlace　and　sf　DarkPlace.　Evaluations　of　both　the　public　benchmarks　and　the　newly　built　benchmarks　show　that　the　task-inspired　design　enables　the　recognizer　to　achieve　significant　performance　improvements　in　the　nighttime　for　robot　place　recognition　compared　to　other　top-ranked　place　recognizers.　©　2024　IEEE.