Robot Self-Calibration Using Actuated 3D Sensors
This addresses the problem of robot calibration for robotics researchers and practitioners by eliminating the need for external devices, though it is incremental as it builds on existing SLAM and ICP methods.
The paper tackles robot calibration by treating it as an offline SLAM problem, enabling fully autonomous self-calibration using only an eye-in-hand depth sensor without external tools, and achieves precision comparable to a dedicated external tracking system at a fraction of the cost.
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.