Visual Navigation System

Visual navigation systems aim to enable robots and autonomous vehicles to navigate using visual input, primarily focusing on robust path planning and obstacle avoidance in dynamic environments. Current research emphasizes the development of deep learning-based approaches, often incorporating techniques like stereo vision, occupancy grid mapping, and reinforcement learning to improve navigation accuracy and generalization across diverse settings. These advancements are crucial for enhancing the capabilities of mobile robots, autonomous vehicles, and unmanned aerial systems in real-world applications, particularly in complex and unstructured environments. The integration of reactive control strategies further enhances safety and adaptability in the face of unexpected obstacles.