Intersecting LiDAR Calibration for Robot Sensor Misalignment

The system enables LiDAR sensors to perform self-calibration by automatically comparing scan data from multiple sensors and computing transformation parameters without human intervention. The processing device autonomously determines pose differences and adjusts sensor data or mountings to achieve proper alignment, eliminating the need for manual operator recalibration while maintaining accuracy.

The system implements a feedback mechanism where scan data from multiple LiDAR sensors is continuously collected and compared. The processing device analyzes discrepancies in localized points between sensors and uses this feedback to automatically compute and apply transformation parameters, creating a closed-loop calibration process that improves alignment accuracy without requiring manual intervention.

The processing device performs multiple functions: it collects scan data from multiple LiDAR sensors, localizes points in the environment, compares data between sensors, computes transformation parameters, and applies corrections. This multi-functional approach consolidates what would otherwise require separate systems into a single integrated processing unit, reducing overall system complexity while enabling autonomous calibration.

The system uses an intermediary flat surface (such as a wall or ground plane) as a common reference frame for multiple LiDAR sensors. By localizing points on this shared surface and comparing transformations relative to it, the system provides a simple geometric mediator that enables autonomous calibration without requiring complex direct sensor-to-sensor communication or alignment mechanisms.

The system creates a reference copy of the environment by localizing points on a flat surface using one LiDAR sensor, then uses this reference to compare and calibrate other sensors. Instead of directly comparing all sensors against each other in a chain that could accumulate errors, the system uses the flat surface as a stable geometric copy of the environment that all sensors can reference, minimizing error propagation.

The system transforms sensor data by computing transformation parameters (rotation and translation) that map scan data from different sensors into a common coordinate frame. By continuously adjusting these parameters based on comparisons with the reference flat surface, the system maintains localization accuracy while enabling rapid automated calibration through parameter optimization rather than physical repositioning.