A Review of Multimodal Sensor Technologies and Fusion Methods for Intelligent Robots

Abstract

With the rapid development of artificial intelligence and robotics, intelligent robots are gradually transitioning from structured environments to open and complex scenarios, posing unprecedented requirements for the depth, breadth, and precision of environmental perception. Single-modal sensors can no longer satisfy the requirements of complex tasks, and multimodal sensor fusion technology has become a key approach to enhance the robot’s environmental perception, state estimation, and decision-making capabilities. This paper presents a systematic review of multimodal sensing technologies and fusion methodologies for intelligent robots. First, the paper outlines the principles and advances of various core sensors; then it delves into key technologies from signal preprocessing to fusion algorithms spanning classical filtering and deep learning; and it synthesizes their application performance in typical scenarios such as navigation, operation, and human-robot collaboration. Finally, confronting current challenges, the paper envisions the future trends of sensing technologies towards intelligence, flexibility, and chipbased development, aiming to offer insights for researchers in the field.