# A Multi-View Three-Dimensional Scanning Method for a Dual-Arm Hand–Eye System with Global Calibration of Coded Marker Points

## TL;DR

A new 3D scanning method improves accuracy in robotic systems by using global calibration and error correction techniques.

## Contribution

The paper introduces a novel global calibration method and multi-view 3D scanning approach for dual-arm robotic systems.

## Key findings

- The proposed method reduces average 3D imaging error to 0.082 mm.
- Global calibration using encoded marker points lowers imaging error by 0.456 mm compared to traditional methods.

## Abstract

To achieve robust and accurate collaborative 3D measurement under complex noise conditions, a global calibration method for dual-arm hand–eye systems and multi-view 3D imaging is proposed. A multi-view 3D scanning approach based on ICP (M3DHE-ICP) integrates a multi-frequency heterodyne coding phase solution with ICP optimization, effectively correcting stitching errors caused by robotic arm attitude drift. After correction, the average 3D imaging error is 0.082 mm, reduced by 0.330 mm. A global calibration method based on encoded marker points (GCM-DHE) is also introduced. By leveraging spatial geometry constraints and a dynamic tracking model of marker points, the transformation between multi-coordinate systems of the dual arms is robustly solved. This reduces the average imaging error to 0.100 mm, 0.456 mm lower than that of traditional circular calibration plate methods. In actual engineering measurements, the average error for scanning a vehicle’s front mudguard is 0.085 mm, with a standard deviation of 0.018 mm. These methods demonstrate significant value for intelligent manufacturing and multi-robot collaborative measurement.