Smartphones　are　increasingly　becoming　an　efficient　platform　for　solving　indoor　positioning　problems.　Fingerprint-based　positioning　methods　are　popular　because　of　the　wide　deployment　of　wireless　local　area　networks　in　indoor　environments　and　the　lack　of　model　propagation　paths.　However,　Wi-Fi　fingerprint　information　is　singular,　and　its　positioning　accuracy　is　typically　2-10　m;　thus,　it　struggles　to　meet　the　requirements　of　high-precision　indoor　positioning.　Therefore,　this　paper　proposes　a　positioning　algorithm　that　combines　Wi-Fi　fingerprints　and　visual　information　to　generate　fingerprints.　The　algorithm　involves　two　steps:　merged-fingerprint　generation　and　fingerprint　positioning.　In　the　merged-fingerprint　generation　stage,　the　kernel　principal　component　analysis　feature　of　the　Wi-Fi　fingerprint　and　the　local　binary　pattern　features　of　the　scene　image　are　fused.　In　the　fingerprint　positioning　stage,　a　light　gradient　boosting　machine　(LightGBM)　is　trained　with　mutually　exclusive　feature　bundling　and　histogram　optimization　to　obtain　an　accurate　positioning　model.　The　method　is　tested　in　an　actual　environment.　The　experimental　results　show　that　the　positioning　accuracy　of　the　LightGBM　method　is　90%　within　a　range　of　1.53　m.　Compared　with　the　single-fingerprint　positioning　method,　the　accuracy　is　improved　by　more　than　20%,　and　the　performance　is　improved　by　more　than　15%　compared　with　other　methods.　The　average　locating　error　is　0.78　m.