With the rapid development of the robotics industry, robotic technology has emerged as a new driving force for enhancing productivity, particularly highlighting the importance of technologies such as 3D reconstruction and obstacle avoidance navigation. However, active 3D imaging technologies based on Time of Flight (ToF) and structured light suffer from limitations such as low resolution, lack of original color information, and and susceptibility to ambient light interference, leading to suboptimal performance. Therefore, passive binocular stereo vision sensors, which can output dense depth and color information (RGB-D) in real-time, have been widely applied in fields such as autonomous robots, automobiles, and drones. Nonetheless, binocular stereo vision technology, which calculates disparity by mimicking human binocular vision for depth information, is computationally intensive and reliant on general-purpose computing platforms. This results in high energy consumption and latency for binocular stereo vision processors, limiting the technology's application in high-speed scenarios, small robots and edge computing. In recent years, binocular stereo vision processors integrated with hardware accelerators for stereo vision algorithms have gained significant attention in both academia and industry. This article systematically explains the theoretical foundation of binocular 3D stereo vision and its application examples in robotic stereo vision in the first section. It then introduces the structural components of binocular stereo vision processors, including core parts such as image acquisition, camera calibration and correction, and stereo matching. For the convenience of stereo vision hardware developers, this paper reviews the basic concepts, research status, challenges, and future trends based on the core components of the binocular stereo vision system, with a special focus on comparing new hardware computing architectures.