AI smart cane technology and assistive navigation for visually impaired users: an overview

Abstract Smart cane technologies have evolved into transformative tools enhancing mobility, safety, and autonomy for individuals with visual impairments. Despite rapid advances, comprehensive surveys are lacking that analyze the evolution of technology, the challenges of integrating these systems, and the design considerations. The objective of this study is to fill that gap by emphasizing the synergy of perception, decision-making, and feedback in smart cane systems, enabled through the integration of sensors, artificial intelligence, and human-centered design, including sensor-rich systems that incorporate Simultaneous Localization and Mapping (SLAM), computer vision, and machine learning techniques. Multimodal feedback is used to provide users with real-time localization, contextual awareness, dynamic obstacle avoidance, and personalized navigation based on their preferences. Several key hardware components are reviewed in the study, including ultrasonic, infrared, LiDAR, RGB-D cameras, and inertial sensors, and sensor fusion strategies are discussed to produce robust spatial perception results. A number of human-centered factors, such as usability, cognitive load, and ergonomics, must be considered in order to assure practical adoption. An integrated smart cane framework is presented that combines edge artificial intelligence, Bluetooth-based indoor localization, 5G/6G secure networking, cloud-based health monitoring, and solar-powered energy management. As a result of this architecture, scalable, intelligent mobility aids are capable of being seamlessly integrated into smart city infrastructures. The paper advocates for a multidisciplinary, systems-level approach to position smart canes as core enablers of inclusive, connected urban environments.