Sensor Fusion
Combining data from multiple sensors (camera, LiDAR, radar, IMU) into a consistent environment model for more robust perception.
Sensor fusion merges camera, LiDAR, and radar data into a complete picture – indispensable for autonomous driving and robotics.
Explanation
Sensor fusion uses Kalman filters, Bayesian inference, or deep learning to merge complementary sensor data. Early fusion (raw data), mid fusion (features), late fusion (decisions) are the main approaches.
Marketing Relevance
Critical for autonomous driving, robotics, AR/VR, and industrial IoT applications – no single sensor is sufficient for safe autonomous decisions.
Common Pitfalls
Time synchronization between sensors, calibration drift, increased system complexity, single point of failure when a sensor fails.
Origin & History
Kalman Filter (1960) laid the mathematical foundation. Military applications drove development until 2000. With autonomous driving (2010s), sensor fusion became a core problem. Deep learning-based fusion (BEVFormer, 2022) significantly improved accuracy.
Comparisons & Differences
Sensor Fusion vs. Computer Vision
Computer vision processes visual data from one sensor; sensor fusion integrates data from multiple heterogeneous sensors.