Pose　estimation　is　the　core　module　of　the　visual　SLAM　system,　which　is　usually　calculated　by　the　front-end　visual　odometry.　The　calculated　pose　gives　the　initial　estimation　of　the　camera　pose　that　is　the　basis　for　later　map　construction　and　path　planning.　The　target　of　pose　estimation　is　to　calculate　the　relative　motion　between　two　frames.　The　absolute　pose　of　the　latter　frame　can　be　calculated　according　to　the　absolute　pose　of　the　previous　frame　and　the　relative　transformation　matrix　of　the　two　frames.　The　main　methods　for　calculating　the　relative　motion　of　two　frames　are　feature-based　method　and　direct　method,　and　both　of　them　have　been　successfully　applied　in　various　visual　SLAM　systems.　But　they　all　have　the　disadvantage　of　needing　to　carefully　adjust　the　parameters,　and　sensitive　to　environment　and　illumination　changes,　image　blur　and　other　factors.　With　the　great　success　of　deep　learning　in　the　field　of　computer　vision,　researchers　began　to　explore　the　use　of　deep　learning　methods　to　solve　pose　estimation　problems.　This　paper　proposes　a　neural　network　model　for　calculating　the　relative　motion　of　two　frames.　The　training　and　testing　are　carried　out　on　a　public　dataset.　The　experimental　results　show　that　the　model　shows　good　performance　for　known　scenes　and　has　certain　generalization　ability　for　unknown　environments,　proving　that　deep　learning　methods　have　the　potential　to　solve　the　problem　of　pose　estimation.