Estimating　the　depth　of　image　and　egomotion　of　agent　are　important　for　autonomous　and　robot　in　understanding　the　surrounding　environment　and　avoiding　collision.　Most　existing　unsupervised　methods　estimate　depth　and　camera　egomotion　by　minimizing　photometric　error　between　adjacent　frames.　However,　the　photometric　consistency　sometimes　does　not　meet　the　real　situation,　such　as　brightness　change,　moving　objects　and　occlusion.　To　reduce　the　influence　of　brightness　change,　we　propose　a　feature　pyramid　matching　loss　(FPML)　which　captures　the　trainable　feature　error　between　a　current　and　the　adjacent　frames　and　therefore　it　is　more　robust　than　photometric　error.　In　addition,　we　propose　the　occlusion-aware　mask　(OAM)　network　which　can　indicate　occlusion　according　to　change　of　masks　to　improve　estimation　accuracy　of　depth　and　camera　pose.　The　experimental　results　verify　that　the　proposed　unsupervised　approach　is　highly　competitive　against　the　state-of-the-art　methods,　both　qualitatively　and　quantitatively.　Specifically,　our　method　reduces　absolute　relative　error　(Abs　Rel)　by　0.017-0.088.