Aiming　at　the　problem　of　partial　occlusion　and　stacking　in　the　field　of　robotic　arm　recognition　and　grasping,　especially　in　non-single　scenes　where　multiple　objects　are　cluttered.　This　paper　proposes　a　recognition　and　grasping　method　based　on　the　improved　DenseFusion　algorithm　network　architecture　to　improve　pose　estimation’s　accuracy　rate　and　combined　with　the　RGB　information　and　depth　information　of　the　depth　camera　to　estimate　the　6D　pose　of　the　target,　and　the　experimental　verification　was　carried　out　on　the　robotic　arm　platform　independently　developed　by　the　laboratory.　The　experimental　results　show　that　the　proposed　method　can　not　only　identify　the　target　object　accurately,　but　also　obtain　the　6D　pose　of　the　target　object　accurately,　so　as　to　improve　the　success　rate　of　grasping　by　the　robotic　arm.　©　2023　SPIE.