Abstract: 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 co-saliency 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.