Recently,　the　map　matching-assisted　positioning　method　based　on　micro-electromechanical　systems　(MEMS)　inertial　devices　has　become　a　research　hotspot　for　indoor　pedestrian　positioning;　however,　these　are　based　on　existing　indoor　electronic　maps.　In　this　paper,　without　prior　knowledge　of　the　map　and　through　building　an　indoor　main　path　feature　point　map　combined　with　the　simultaneous　localization　and　map　building　(SLAM)　particle　filter　(PF-SLAM)　algorithm　idea,　a　PF-SLAM　indoor　pedestrian　location　algorithm　based　on　a　feature　point　map　was　proposed　through　the　inertial　measurement　unit　to　improve　indoor　pedestrian　positioning　accuracy.　Aiming　at　the　problem　of　inaccurate　heading　angle　estimation　in　the　pedestrian　dead　reckoning　(PDR)　algorithm,　a　turn-straight-state　threshold　detection　method　was　proposed　that　corrected　the　difference　of　the　heading　angles　during　the　straight-line　walking　of　pedestrians　to　suppress　the　error　accumulation　of　the　heading　angle.　Aiming　at　the　particles　that　are　severely　divergent　at　the　corners,　a　feature　point　matching　algorithm　was　proposed　to　correct　the　pedestrian　position　error.　Furthermore,　the　turning　point　extracted　the　main　path　that　failed　to　match　the　current　feature　point　map　as　a　new　feature　point　was　added　to　update　the　map.　Through　the　mutual　modification　of　SLAM　and　an　inertial　navigation　system　(INS)　the　long-time,　high-precision,　and　low-cost　positioning　functions　of　indoor　pedestrians　were　realized.