The auditory system is considered one of the crucial pathways for robots to perceive environmental information. The perception and decision-making capabilities of mobile robots are greatly enhanced by accurate and effective sound source localization, making it highly significant for applications in hazardous environment rescue and inspection. With the widespread adoption of deep learning, the effectiveness of sound source localization has been notably improved through the introduction of convolutional neural networks (CNNs). Sound source localization for mobile robots was comprehensively compared and analyzed from four perspectives: network architecture and improvements, types of sound features, data simulation and augmentation, as well as the fusion of multimodal information. Reflections and prospects on the application of the technology are also presented.