Aiming　at　the　problem　that　the　original　ORBSLAM　algorithm　only　uses　point　features　to　estimate　pose,　which　leads　to　the　degradation　of　the　algorithm　accuracy　and　robustness　in　the　scene　with　insufficient　point　features.　In　order　to　improve　the　efficiency　and　accuracy　of　the　algorithm,　the　original　algorithm　is　improved.　Firstly,　this　paper　builds　a　binary　environment　dictionary,　which　can　increase　the　dictionary　loading　speed　of　the　algorithm.　Secondly,　an　improved　RANSAC　algorithm　is　proposed　to　increase　the　elimination　efficiency　in　eliminating　feature　mismatching.　Then,　a　hybrid　feature　model　is　established　to　extract　the　point　features　and　line　features　in　the　image　to　estimate　pose.　Finally,　according　to　the　pose　graph　optimization　model,　a　global　Bundle　Adjustment　algorithm　based　on　the　maximum　common　view　weight　frames　is　proposed　and　used　in　pose　optimization,　and　the　establishing　of　the　dense　and　sparse　maps　is　realized.　The　experiment　results　show　that　the　hybrid　feature　model　can　effectively　reduce　the　algorithm　error.　With　the　binary　environment　dictionary,　the　loading　time　of　the　dictionary　is　decreased　from　14.945　seconds　to　0.420　seconds.　The　execution　time　of　the　improved　RANSAC　algorithm　is　reduced　from　0.803　seconds　to　0.078　seconds.　Using　TUM　dataset　to　verify　the　algorithm,　the　root　mean　square　error　of　the　camera　trajectory　is　decreased　by　2.27　cm　and　4.87　cm,　respectively.　The　experiment　results　on　the　dataset　prove　the　effectiveness　of　the　proposed　algorithm.　©　2018,　Science　Press.　All　right　reserved.