A　visual-inertial　odometer　is　used　to　fuse　the　image　information　obtained　by　a　vision　sensor　with　the　data　measured　by　an　inertial　sensor　and　recover　the　motion　track　online　in　a　global　frame.　However,　in　an　indoor　environment,　geometric　transformation,　sparse　features,　illumination　changes,　blurring,　and　noise　will　occur,　which　will　either　cause　a　reduction　in　or　failure　of　the　positioning　accuracy.　To　solve　this　problem,　a　map　matching　algorithm　based　on　an　indoor　plane　structure　map　is　proposed　to　improve　the　positioning　accuracy　of　the　system;　this　algorithm　was　implemented　using　a　conditional　random　field　model.　The　output　of　the　attitude　information　from　the　visual-inertial　odometer　was　used　as　the　input　of　the　conditional　random　field　model.　The　feature　function　between　the　attitude　information　and　the　expected　value　was　established,　and　the　maximum　probabilistic　value　of　the　attitude　was　estimated.　Finally,　the　closed-loop　feedback　correction　of　the　visual-inertial　system　was　carried　out　with　the　probabilistic　attitude　value.　A　number　of　experiments　were　designed　to　verify　the　feasibility　and　reliability　of　the　positioning　method　proposed　in　this　paper.