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Multimodal Perception and Data Fusion: The Core Engine of Intelligent UAVs

2026-02-23

Multimodal Perception and Data Fusion: The Core Engine of Intelligent UAVs

Perception has become the central driver of drone intelligence, supported by breakthroughs in multimodal sensing and data fusion. Single-sensor systems fall short in complex dynamic environments, making vision–LiDAR fusion a key advancement.

Baya et al. combined CNNs with LiDAR data to greatly improve dynamic obstacle detection and tracking, boosting flight safety in highly dynamic scenarios (Fig. 3(a)). Ullah et al. further refined the vision–LiDAR fusion strategy, extending its focus from dynamic obstacle recognition to multi-environment adaptation, expanding support for diverse complex missions.

If perception lets drones “see clearly,” data fusion ensures they “see precisely.” Xu et al. developed a multimodal neural network fusion framework that updates environmental models in real time and optimizes path planning, with strong adaptability in rough terrain. Jiang et al. complemented this work with a multi-scale infrared–vision fusion algorithm for low-visibility conditions. Together, these advances strengthen real-time perception, environmental modeling, and mission reliability for autonomous drones.

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FPV Video Transmitter

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Multimodal Perception and Data Fusion: The Core Engine of Intelligent UAVs

Multimodal Perception and Data Fusion: The Core Engine of Intelligent UAVs

Multimodal Perception and Data Fusion: The Core Engine of Intelligent UAVs