Pose estimation for objects is employed in rich artificial intelligence fields, such as robotics, unmanned driving, aerospace, and virtual reality. This paper mainly discusses the mainstream measurement systems and methods in term of research status, frontier trends, and hot issues, the differences, advantages and disadvantages of which are compared and analyzed in detailed. In general, virtual-camera-based pose estimation systems have outstanding system integration while providing with high accuracy and low cost. Deep-learning-based methods exhibit excellent performance in adaptability of scenes and objects, which are expected to be widely used in unstructured industrial scenarios. Finally, starting from the perception of scenes and objects, this paper analyzes many severe challenges faced by the current pose estimation technology, and looks forward to the research focus and direction of pose estimation technology.