To　improve　the　accuracy　and　reliability　of　the　grasping　pose　generated　by　the　grasping　detection　model,　in　this　paper,　the　GPD(Grasp　pose　detection　in　point　clouds)　is　targeted　at　the　problem　that　rough　object　detection　may　treat　multiple　objects　as　one　object　to　generate　wrong　grasping　pose　and　fail　to　generate　grasping　pose　at　the　edge　of　point　clouds　with　thin　walls.　A　grasping　pose　detection　method　combining　instance　segmentation　and　sampling　based　on　point　cloud　normal　vector　and　principal　curvature　was　proposed.　First,　the　Yolov7　instance　segmentation　network　is　used　to　generate　object　masks　for　RGB　images　collected　by　the　KinectV2　camera　and　crop　out　the　point　cloud　where　the　object　needs　to　be　grasped.　The　sampling　candidate　grasping　the　pose　of　the　point　cloud　is　performed　based　on　the　normal　vector　and　principal　curvature　geometric　features　of　the　point　cloud.　Furthermore,　PointNet++　was　used　to　evaluate　the　network　classification　of　grasping　pose　by　feature　extraction.　Finally,　collision　detection　was　carried　out　on　positive　tag　grasping　pose　to　screen　out　non-collision　grasping　pose.　Experimental　results　show　that　the　grasping　pose　detection　method　integrated　with　instance　segmentation　in　this　paper　has　a　higher　success　rate　than　GPD　in　generating　a　feasible　grasping　pose,　and　can　solve　the　problem　that　GPD　cannot　generate　the　grasping　pose　at　the　edge　of　point　cloud　with　thin　walls.　©　2023　IEEE.