随着机器人产业的快速发展,机器人技术已成为推动生产力提升的新动力,特别是三维重建、避障导航等技术的重要性日益凸显。基于飞行时间(Time of Flight, ToF)和结构光等主动式三维成像技术受限于自身分辨率较低、缺乏色彩信息和易受环境光干扰等因素,表现不够理想。因此,能够实时精确输出稠密深度和色彩信息(RGB-D)的被动式双目立体视觉传感器在自主移动机器人、汽车和微型无人机等领域得到了广泛应用。然而,双目立体视觉技术通过模仿人类双眼计算视差来提供深度信息,计算复杂度高且依赖于通用计算平台,导致双目立体视觉处理器面临着高能耗和高延迟等问题,这限制了该技术在高速场景、小型机器人和边缘计算等领域的应用。近年来,那些集成了立体视觉算法专用硬件加速器的双目立体视觉处理器在学术界和产业界引起了广泛关注。本文首先系统阐述了双目三维立体视觉的理论基础及其在机器人立体视觉的应用实例,接着介绍了双目立体视觉处理器的组成结构,包括图像获取、相机标定与校正、立体匹配等核心部分。为便于立体视觉硬件开发者参考,本文根据双目立体视觉系统的核心组成结构,分别综述了基本概念、研究现状和难点与挑战,并特别关注和对比了新型硬件计算架构。

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.