Mobile Robot Environment Perception System Based on Multimodal Sensor Fusion

Abstract

In the context of rapid technological advancement, mobile robots' applications are expanding across intelligent manufacturing, autonomous driving, and disaster rescue, demanding enhanced environmental perception capabilities. Environmental perception systems based on Multimodal Sensor Fusion technology effectively improve mobile robots' understanding and adaptability in complex environments. Multimodal sensor fusion integrates data from multiple sensors to overcome individual sensor limitations. Through analysis of data fusion algorithms and real-time processing technology, efficient information extraction and noise reduction enhance mobile robot adaptability in dynamic environments. Case studies demonstrate that environmental perception systems incorporating deep learning and computer vision technologies achieve high-precision obstacle recognition and path planning across diverse settings. The system improves complex scene comprehension and autonomous navigation capabilities through neural network-based feature extraction models. Through systematic theoretical frameworks and case analysis, the research explores multimodal sensor fusion's potential and practical effects in mobile robot environmental perception systems, providing fundamental data support and theoretical foundations for future research developments.