Spatial Calibration

Spatial calibration focuses on accurately aligning the coordinate systems of different sensors or systems, crucial for applications requiring precise spatial awareness. Current research emphasizes automated calibration techniques, employing methods like target-based registration for near-field sensor fusion (e.g., radar and optical sensors) and context-based matching for multi-agent cooperative perception. Advanced approaches utilize neural networks, such as neural distortion fields, to model and correct complex geometric distortions, particularly in high-resolution displays. These advancements improve the accuracy and efficiency of sensor data fusion, impacting fields like autonomous driving, augmented reality, and robotics.