This paper presents a novel method of autonomous grasping design for a mobile manipulator, such that the robot can find and grasp a target object in a complex environment. Scale invariant feature transform (SIFT) algorithm is adopted to search and recognize features of the object to be grasped. Histogram-enhanced feature matching (HEFM) is developed to obtain depth estimate and reject unreliable feature points in order to improve the feature matching accuracy. The concept of virtual points is proposed to facilitate image-based visual servo controller design. Experimental results of autonomous object grasping validate the proposed method.