By　the　traditional　algorithm　model,　the　service　robot　performs　detection,　identification　and　location　successively,　which　causes　low　execution　efficiency.　In　order　to　solve　this　problem,　a　fast　spatial　object　location　method　based　on　cosaliency　detection　is　proposed　for　service　robots.　Firstly,　N　pairs　of　RGB　images　and　depth　images　containing　the　object　to　be　located　are　obtained　by　using　RGB-D　sensor,　and　the　object　to　be　located　is　regarded　as　the　co-salient　target.　Through　fully　exploring　the　saliency　propagation　mechanism　of　a　single　image　in　RGB　images,　a　two-stage　guided　co-saliency　detection　model　is　constructed　on　the　basis　of　inter-image　saliency　propagation　and　intra-image　manifold　ranking.　At　the　same　time,　the　background　and　non-collaborative　salient　objects　are　excluded　to　get　pixel　coordinate　set　of　the　co-salient　object　region.　Then,　the　spatial　coordinates　of　the　object　centroid　are　determined　by　the　correspondence　between　the　RGB　image　and　the　depth　image　to　achieve　the　fast　location　of　the　spatial　object.　Finally,　some　experiments　are　performed　on　iCoseg　standard　database　and　the　service　robot　manipulator　grasping　platform　with　hand-eye　calibration.　The　experiment　results　show　that　the　proposed　method　is　superior　to　the　current　five　co-saliency　detection　algorithms,　can　meet　the　real-time　needs　of　the　gripping　system,　and　has　better　accuracy　and　robustness　in　the　cases　of　complicated　background,　multi-target　interference　and　varying　illumination.　©　2017,　Science　Press.　All　right　reserved.