High-precision　indoor　localization　plays　a　vital　role　in　various　places.　In　recent　years,　visual　inertial　odometry　(VIO)　system　has　achieved　outstanding　progress　in　the　field　of　indoor　localization.　However,　it　is　easily　affected　by　poor　lighting　and　featureless　environments.　For　this　problem,　we　propose　an　indoor　localization　algorithm　based　on　VIO　system　and　three-dimensional　(3D)　map　matching.　The　3D　map　matching　is　to　add　height　matching　on　the　basis　of　previous　two-dimensional　(2D)　matching　so　that　the　algorithm　has　more　universal　applicability.　Firstly,　the　conditional　random　field　model　is　established.　Secondly,　an　indoor　three-dimensional　digital　map　is　used　as　a　priori　information.　Thirdly,　the　pose　and　position　information　output　by　the　VIO　system　are　used　as　the　observation　information　of　the　conditional　random　field　(CRF).　Finally,　the　optimal　states　sequence　is　obtained　and　employed　as　the　feedback　information　to　correct　the　trajectory　of　VIO　system.　Experimental　results　show　that　our　algorithm　can　effectively　improve　the　positioning　accuracy　of　VIO　system　in　the　indoor　area　of　poor　lighting　and　featureless.