To　deal　with　the　challenges　of　object　detection　and　object　grabbing　tasks　for　service　robots,　we　propose　an　object　detection　and　segmentation　algorithm　based　on　the　deep　convolutional　neural　network　(CNN)　and　depth-first　algorithm　to　achieve　object　grabbing　by　using　an　RGB-D　camera　and　the　six-degree-of-freedom　robotic　manipulator.　Firstly,　the　improved　particle　swarm　optimization　(PSO)　algorithm　is　proposed　to　calibrate　and　optimize　the　hand-eye　system　of　the　experimental　platform,　which　is　a　mobile　robot　equipped　with　UR5　mechanical　arm　and　Kinect　V2　sensor.　Then,　we　collect　the　environmental　information　by　the　camera,　where　the　depth　images　restored　by　the　joint　bilateral　filtering　algorithm　and　the　original　color　images　are　calibrated.　Finally,　a　depth　learning　method　is　used　to　detect　the　objects,　and　the　depth　information　is　used　to　achieve　objects　segmentation.　We　complete　object　grabbing　based　on　the　estimated　3D　coordinates,　which　has　proven　the　practicability　and　effectiveness　of　our　proposed　grabbing　framework.　©　2018　IEEE.