Indoor　localization　and　tracking　plays　a　vital　role　in　the　IoT　field　for　its　various　types　of　applications,　e.g.　healthcare　system,　pedestrian　navigation,　emergency　rescue.　Among　all　the　available　sensors,　the　combination　of　IMU　and　WiFi-RSS　is　the　most　widely　used　scheme　for　its　low　cost　and　pedestrian　compatibility.　However,　conventional　indoor　localization　systems　cannot　achieve　satisfactory　accuracy.　To　cope　with　this　issue,　in　this　paper,　we　propose　a　novel　indoor　localization　system　that　integrates　the　IMU　sensing　and　the　RSS　fingerprinting　via　a　proposed　PSO　(Particle　Swarm　Optimization)　based　algorithm.　We　develop　a　new　WiFi-enabled　platform　which　can　obtain　RSS　from　captured　wireless　messages　in　the　WiFi　traffic.　Based　on　this　platform,　the　RSS　fingerprints　and　the　displacement　estimated　by　IMU　serve　as　the　guidance　and　soft　constraints　for　the　particle　swarm.　A　fitness　metric　is　also　proposed　to　evaluate　the　likelihood　of　each　particle　finding　the　real　position　by　incorporating　the　GPR　(Gaussian　Process　Regression)　method.　To　improve　the　localization　accuracy,　we　leverage　the　layout　of　the　map,　which　provides　information　on　accessible　and　inaccessible　areas.　Besides,　image　processing　techniques　including　line　detection　and　contour　extraction　are　utilized　to　constrain　the　particles　that　may　fall　into　the　inaccessible　areas.　Experiments　are　conducted　in　a　multiple　function　laboratory　and　the　results　verify　that　the　proposed　approach　outperforms　the　typical　approaches,　and　the　achieved　mean　localization　error　is　0.705m.　©　2022　IEEE.