Accurate　indoor　pedestrian　localization　and　tracking　are　crucial　in　many　practical　applications.　One　efficient　yet　low-cost　sensing　scheme　is　the　integration　of　inertial　measurement　unit　and　WiFi　received　signal　strength　(RSS)　due　to　the　popularity　of　smart　devices　and　WiFi　networks.　Many　approaches　have　been　proposed　to　enhance　the　localization　performance.　However,　they　heavily　rely　on　prerequisites,　including　prior　knowledge　(e.g.,　map　information)　and　beacon　corrections,　which　degrades　the　generalization　of　the　approaches　and　their　accuracy　in　complex　environments.　To　address　this　issue,　in　this　article,　we　propose　a　novel　localization　approach　named　VariFi,　which　incorporates　variational　inference　techniques　to　estimate　the　location　of　pedestrian.　Variational　inference　is　applied　in　this　work,　whose　inference　network　can　produce　accurate　estimates　as　its　parameters　are　optimized　in　terms　of　the　reconstruction　loss　and　regularization　loss　in　real　time.　A　signal　map　is　constructed　to　provide　a　conditional　RSS　distribution　at　any　given　location,　which　is　further　applied　to　generate　the　reconstruction　loss　based　on　the　real　measurements.　Also,　a　filtering　mechanism　is　designed　to　reduce　local　optimum　cases　in　optimization　by　utilizing　the　prior　estimate　and　RSS　fingerprinting　estimate.　In　addition,　VariFi　can　be　further　applied　to　conduct　online　optimization　following　the　existing　localization　approaches.　We　conduct　experiments,　including　static　localization　and　trajectory　estimation　scenarios　to　validate　the　performance　of　our　approach.　The　trajectory　estimation　results　show　that　our　approach　outperforms　the　mainstream　approaches　in　terms　of　both　localization　accuracy　and　robustness,　respectively.　Furthermore,　the　combination　of　existing　approaches　and　VariFi　has　also　been　validated　effectively　in　the　experiments　of　two　environments,　where　VariFi　has　the　ability　to　bring　enhanced　localization　accuracy.