SCALAR - Simultaneous Calibration of 2D Laser and Robot's Kinematic Parameters Using Three Planar Constraints

TL;DR

SCALAR is a calibration method that simultaneously refines a robot's kinematic parameters and a 2D laser scanner's extrinsic parameters using planar constraints, improving accuracy without external measurement systems.

Contribution

The paper introduces SCALAR, a novel calibration approach that uses three planar constraints and nonlinear optimization to calibrate both robot and sensor parameters simultaneously.

Findings

Significant reduction in position errors from 14.6mm to 0.09mm.

Orientation errors decreased from 4.05° to 0.02°.

Effective calibration without external measurement systems.

Abstract

Industrial robots are increasingly used in various applications where the robot accuracy becomes very important, hence calibrations of the robot's kinematic parameters and the measurement system's extrinsic parameters are required. However, the existing calibration approaches are either too cumbersome or require another expensive external measurement system such as laser tracker or measurement spinarm. In this paper, we propose SCALAR, a calibration method to simultaneously improve the kinematic parameters of a 6-DoF robot and the extrinsic parameters of a 2D Laser Range Finder (LRF) which is attached to the robot. Three flat planes are placed around the robot, and for each plane the robot moves to several poses such that the LRF's ray intersect the respective plane. Geometric planar constraints are then used to optimize the calibration parameters using Levenberg- Marquardt nonlinear optimization algorithm. We demonstrate through simulations that SCALAR can reduce the average position and orientation errors of the robot system from 14.6mm and 4.05 degrees to 0.09mm and 0.02 degrees.