In　response　to　the　poor　positioning　performance　and　errors　of　the　wheeled　robot　under　a　single　sensor,　a　combination　of　wheel　odometry　dead　reckoning,　Inertial　Measurement　Unit　(IMU)　heading　angle　information,　and　map　environment　information　obtained　from　LiDAR　is　used　for　indoor　positioning　of　the　wheeled　robot.　By　using　data　from　multiple　sensors,　errors　are　reduced　and　positioning　accuracy　is　improved.　The　results　show　that　combining　the　Extended　Kalman　Filter　(EKF),　Gmapping　algorithm,　and　Adaptive　Monte　Carlo　Localization　(AMCL)　reduces　the　relative　error　by　8.35　%　compared　to　the　single　wheel　odometry　heading　calculation　method.　Compared　with　the　EKF　fusion　method　for　dead　reckoning　and　inertial　measurement　unit　heading　angle　information,　the　relative　error　is　reduced　by　4　%,　and　there　is　no　cumulative　trend　in　angle　error.　This　effectively　addresses　the　challenge　of　cumulative　errors　in　indoor　positioning　within　environments　that　lack　base　stations,　thereby　enhancing　accuracy　and　reliability　in　such　specialized　conditions.