Traditional　monocular　direct　visual　odometry　(DVO)　is　one　of　the　most　famous　methods　to　estimate　the　ego-motion　of　robots　and　map　environments　from　images　simultaneously.　However,　DVO　heavily　relies　on　high-quality　images　and　accurate　initial　pose　estimation　during　tracking.　With　the　outstanding　performance　of　deep　learning,　previous　works　have　shown　that　deep　neural　networks　can　effectively　learn　6-DoF　(Degree　of　Freedom)　poses　between　frames　from　monocular　image　sequences　in　the　unsupervised　manner.　However,　these　unsupervised　deep　learning-based　frameworks　cannot　accurately　generate　the　full　trajectory　of　a　long　monocular　video　because　of　the　scale-inconsistency　between　each　pose.　To　address　this　problem,　we　use　several　geometric　constraints　to　improve　the　scale-consistency　of　the　pose　network,　including　improving　the　previous　loss　function　and　proposing　a　novel　scale-to-trajectory　constraint　for　unsupervised　training.　We　call　the　pose　network　trained　by　the　proposed　novel　constraint　as　TrajNet.　In　addition,　a　new　DVO　architecture,　called　deep　direct　sparse　odometry　(DDSO),　is　proposed　to　overcome　the　drawbacks　of　the　previous　direct　sparse　odometry　(DSO)　framework　by　embedding　TrajNet.　Extensive　experiments　on　the　KITTI　dataset　show　that　the　proposed　constraints　can　effectively　improve　the　scale-consistency　of　TrajNet　when　compared　with　previous　unsupervised　monocular　methods,　and　integration　with　TrajNet　makes　the　initialization　and　tracking　of　DSO　more　robust　and　accurate.