关键词: AGV, 图像点云融合, 深度学习, 环境感知, 人工智能

Abstract: With the rapid development of automated industrial production lines and intelligent logistics and warehousing systems, Automated Guided Vehicle (AGV) serve as core components in industrial automation and smart manufacturing. The environmental perception capability constitutes a prerequisite for achieving high-precision autonomous navigation and intelligent operations. The deep integration of artificial intelligence and multimodal sensing technologies enables AGV to overcome the limitations of traditional perception modes, realizing dynamic understanding and autonomous responses in complex industrial scenarios. Image-point cloud fusion perception technology has emerged as a critical breakthrough direction for enhancing AGV perception robustness and adaptability, leveraging its advantages in 3D spatial resolution and semantic information complementarity. The evolutionary trajectory of image-point cloud fusion technology in AGV environmental perception is systematically reviewed, while performance boundaries and application scenarios of typical fusion strategies are compared, along with deep learning-based fusion methodologies being analyzed. Bottleneck challenges including multi-sensor calibration and long-term stability, algorithm real-time performance, conflicts between edge computing resources and algorithm complexity, and extreme condition adaptability are addressed, with technical development directions being proposed from four dimensions: unsupervised online calibration, dynamic perception-decision closed-loop optimization, algorithm-task co-design, and adaptive perception in extreme scenarios. Theoretical foundations and technical references are provided for the construction of safer, more efficient, and universally applicable AGV environmental perception systems through these proposed approaches.

Key words: AGV, image-point cloud fusion, deep learning, environmental perception, artificial intelligence