Foot-mounted　micro-electromechanical　systems　(MEMS)　inertial　sensors　based　on　pedestrian　navigation　can　be　used　for　indoor　localization.　We　previously　developed　a　novel　zero-velocity　detection　algorithm　based　on　the　variation　in　speed　over　a　gait　cycle,　which　can　be　used　to　correct　positional　errors.　However,　the　accumulation　of　heading　errors　cannot　be　corrected　and　thus,　the　system　suffers　from　considerable　drift　over　time.　In　this　paper,　we　propose　a　map-matching　technique　based　on　conditional　random　fields　(CRFs).　Observations　are　chosen　as　positions　from　the　inertial　navigation　system　(INS),　with　the　length　between　two　consecutive　observations　being　the　same.　This　is　different　from　elsewhere　in　the　literature　where　observations　are　chosen　based　on　step　length.　Thus,　only　four　states　are　used　for　each　observation　and　only　one　feature　function　is　employed　based　on　the　heading　of　the　two　positions.　All　these　techniques　can　reduce　the　complexity　of　the　algorithm.　Finally,　a　feedback　structure　is　employed　in　a　sliding　window　to　increase　the　accuracy　of　the　algorithm.　Experiments　were　conducted　in　two　sites　with　a　total　of　over　450　m　in　travelled　distance　and　the　results　show　that　the　algorithm　can　efficiently　improve　the　long-term　accuracy.