In　recent　years,　mobile　robots　have　found　extensive　application　across　diverse　sectors　including　industry,　agriculture,　healthcare,　and　defense.　However,　relying　solely　on　a　single　sensor　for　mobile　robot　localization　presents　several　challenges,　such　as　limited　accuracy,　divergence　of　localization　errors　over　time,　and　susceptibility　to　obstruction　from　obstacles.　This　paper　proposes　an　indoor　mobile　robot　localization　algorithm　assisted　by　Ultra-Wideband　(UWB).　The　algorithm　begins　by　calculating　the　credibility　of　the　Line-of-Sight　(LOS)　environment　using　UWB　ranging　measurements　and　predicted　distances,　enabling　the　identification　of　the　Non-Line-of-Sight　(NLOS)　environment.　Subsequently,　ranging　measurements　affected　by　NLOS　errors　are　compensated　by　using　a　complementary　filter.　Finally,　these　measurements　are　utilized　for　Extended　Kalman　Filter　updates　to　achieve　the　best　estimation　of　the　mobile　robot’s　position.　Field　tests　are　conducted　on　a　wheeled　robot　to　validate　the　effectiveness　and　performance　of　the　developed　approach.　Results　show　that　the　localization　approach　reduces　the　maximum　localization　error　from　41　cm　to　19　cm　compared　to　UWB　trilateration,　achieving　a　53.7%　improvement.　Even　under　a　prolonged　NLOS　environment,　the　algorithm　ensures　that　the　localization　error　remains　below　25　cm.　The　proposed　method　is　of　significant　merit　in　solving　the　challenge　of　indoor　mobile　robot　localization.