This paper reviews sensing technologies for indoor autonomous mobile robots (AMRs). It discusses the application of various sensors, including inertial measurement units (IMUs), ultrasonic sensors, infrared sensors, LiDAR, and vision-based sensors, in tasks such as localization, mapping, obstacle avoidance, and navigation. The paper analyzes the advantages and limitations of using single sensors and introduces multi-sensor fusion techniques to improve accuracy and reliability. It also discusses the principles and algorithms used in processing sensor data, such as filter-based and graph optimization-based SLAM methods. The paper highlights the importance of perception in mobile robotics, emphasizing the need for accurate and efficient environmental sensing to enable safe and effective navigation. It also explores the potential of artificial intelligence, such as reinforcement learning, in improving navigation capabilities by reducing reliance on prior maps. The paper discusses the challenges and future trends in sensing technologies for AMRs, including the development of more accurate and robust sensors, the integration of multi-sensor fusion, and the application of advanced algorithms for improved perception and navigation. The review also covers the use of vision-based sensors for obstacle detection and mapping, as well as the advantages and limitations of different types of sensors in indoor environments. Overall, the paper provides a comprehensive overview of the current state of sensing technologies for AMRs and highlights the key challenges and opportunities in this field.This paper reviews sensing technologies for indoor autonomous mobile robots (AMRs). It discusses the application of various sensors, including inertial measurement units (IMUs), ultrasonic sensors, infrared sensors, LiDAR, and vision-based sensors, in tasks such as localization, mapping, obstacle avoidance, and navigation. The paper analyzes the advantages and limitations of using single sensors and introduces multi-sensor fusion techniques to improve accuracy and reliability. It also discusses the principles and algorithms used in processing sensor data, such as filter-based and graph optimization-based SLAM methods. The paper highlights the importance of perception in mobile robotics, emphasizing the need for accurate and efficient environmental sensing to enable safe and effective navigation. It also explores the potential of artificial intelligence, such as reinforcement learning, in improving navigation capabilities by reducing reliance on prior maps. The paper discusses the challenges and future trends in sensing technologies for AMRs, including the development of more accurate and robust sensors, the integration of multi-sensor fusion, and the application of advanced algorithms for improved perception and navigation. The review also covers the use of vision-based sensors for obstacle detection and mapping, as well as the advantages and limitations of different types of sensors in indoor environments. Overall, the paper provides a comprehensive overview of the current state of sensing technologies for AMRs and highlights the key challenges and opportunities in this field.