Robot Coordinate Calibration Using Multi-Point Visual Transform Equivalence

The calibration process is segmented into multiple sampling points (first sampling point, second sampling point, etc.) where transformation relationships are obtained separately and then integrated. This segmentation allows for systematic data collection and processing, improving calibration efficiency by breaking down the complex calibration task into manageable segments that can be processed independently and then combined through the resolving module.

A calibration object coordinate system is introduced as an intermediary between the visual perception coordinate system and the ontology coordinate system. The resolving module uses this intermediary to establish the transformation relationship by resolving unknown variables based on equivalence relationships between transformation relationships obtained at different sampling points. This intermediary approach enables efficient calibration without requiring direct complex transformations between the visual and ontology coordinate systems.

The resolving module uses feedback from multiple sampling points to iteratively resolve the unknown variable (transformation relationship between visual perception coordinate system and ontology coordinate system). By obtaining transformation relationships at multiple sampling points and using equivalence relationships to resolve the unknown variable, the system achieves high calibration accuracy while managing complexity through a systematic feedback-based resolution process.

The calibration method changes parameters by collecting transformation relationships at multiple different sampling points (different positions of the target motion mechanism). This parameter change approach allows the system to obtain sufficient information for accurate calibration while the resolving module manages the complexity by systematically processing these parameter variations to resolve the unknown transformation relationship.