The paper reviews brain-inspired cognition and navigation technology for mobile robots, emphasizing the integration of environmental perception, spatial cognition, and target navigation. It highlights the use of various sensors for environmental data collection and multimodal information fusion to enhance perception. The paper discusses the challenges in achieving high navigation success rates and efficiency, and the need for further exploration of brain-inspired mechanisms. It provides a systematic study on brain-inspired environment perception, spatial cognition, and goal-oriented navigation, offering a new classification of techniques and a theoretical basis for future research. The paper also analyzes state-of-the-art technologies and their biological mechanisms, including place cells, grid cells, and head direction cells, and their roles in navigation. Additionally, it explores brain-inspired environmental perception, spatial cognition using deep learning, continuous attractor neural networks, and spiking neural networks, as well as goal-based navigation techniques such as supervised learning and deep reinforcement learning. The paper concludes by emphasizing the multidisciplinary nature of brain-inspired navigation technology and the need for further collaboration among researchers.The paper reviews brain-inspired cognition and navigation technology for mobile robots, emphasizing the integration of environmental perception, spatial cognition, and target navigation. It highlights the use of various sensors for environmental data collection and multimodal information fusion to enhance perception. The paper discusses the challenges in achieving high navigation success rates and efficiency, and the need for further exploration of brain-inspired mechanisms. It provides a systematic study on brain-inspired environment perception, spatial cognition, and goal-oriented navigation, offering a new classification of techniques and a theoretical basis for future research. The paper also analyzes state-of-the-art technologies and their biological mechanisms, including place cells, grid cells, and head direction cells, and their roles in navigation. Additionally, it explores brain-inspired environmental perception, spatial cognition using deep learning, continuous attractor neural networks, and spiking neural networks, as well as goal-based navigation techniques such as supervised learning and deep reinforcement learning. The paper concludes by emphasizing the multidisciplinary nature of brain-inspired navigation technology and the need for further collaboration among researchers.