Robot Self-Calibration Using Actuated 3D Sensors

TL;DR

This paper introduces a novel autonomous self-calibration method for robots using only an in-hand 3D sensor, framing the problem as an offline SLAM task and achieving high precision without external calibration tools.

Contribution

It presents a new approach that treats robot calibration as an offline SLAM problem, enabling fully autonomous calibration with arbitrary eye-in-hand depth sensors.

Findings

Achieves calibration precision comparable to external tracking systems

Eliminates need for external calibration objects or markers

Demonstrates effectiveness on real robots with various 3D sensors

Abstract

Both, robot and hand-eye calibration haven been object to research for decades. While current approaches manage to precisely and robustly identify the parameters of a robot's kinematic model, they still rely on external devices, such as calibration objects, markers and/or external sensors. Instead of trying to fit the recorded measurements to a model of a known object, this paper treats robot calibration as an offline SLAM problem, where scanning poses are linked to a fixed point in space by a moving kinematic chain. As such, the presented framework allows robot calibration using nothing but an arbitrary eye-in-hand depth sensor, thus enabling fully autonomous self-calibration without any external tools. My new approach is utilizes a modified version of the Iterative Closest Point algorithm to run bundle adjustment on multiple 3D recordings estimating the optimal parameters of the kinematic model. A detailed evaluation of the system is shown on a real robot with various attached 3D sensors. The presented results show that the system reaches precision comparable to a dedicated external tracking system at a fraction of its cost.