Multi-Sensor Fusion: Paradigm Shift from Automotive to Smart Factories
Multi-Sensor Fusion: Paradigm Shift from Automotive to Smart Factories
Technical Essence: Complementary data fusion (LiDAR geometry + camera semantics + radar motion) overcomes limitations of single sensors.
Industrial vs. Automotive Applications:
| Parameter | Industrial Automation | Autonomous Vehicles |
|---|---|---|
| Precision | ±0.1mm (precision assembly) | ±10cm (positioning) |
| Data Sources | 2D vision + force sensors (90%) | LiDAR + radar + IMU (core) |
| Fusion Level | Low-level (raw data fusion) | High-level (object fusion) |
Industrial Cases:
Lithium Battery Defect Detection: Infrared thermal imaging (temperature anomalies) + high-res cameras (surface cracks), reducing miss rate to <0.01% (vs. **>2%** with single sensors).
AGV Obstacle Avoidance: 2D LiDAR (planar positioning) + ToF depth cameras (3D obstacles), achieving positioning error ±3mm.
Algorithm Evolution:
Industry: Traditional Kalman filters (low computation, fixed scenarios).
Automotive: Deep learning fusion (e.g., BEVFormer) for end-to-end multimodal processing.
Future Convergence:
5G + Edge Computing: Integrating IoT microchips into Cage Clamp terminals for real-time vibration data transmission to PLCs.
Unified Perception Framework: BEV fusion algorithms from autonomous driving migrating to smart factories for dynamic environment awareness.
