Robot self‐calibration using actuated 3D sensors

Abstract

Both robot and hand‐eye calibration have been object of 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 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 via a moving kinematic chain. As such, we enable robot calibration by using nothing but an arbitrary eye‐in‐hand depth sensor. To the authors' best knowledge the presented framework is the first solution to three‐dimensional (3D) sensor‐based robot calibration that does not require external sensors nor reference objects. Our novel approach utilizes a modified version of the Iterative Corresponding 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.

Adaptive Graph Convolution Algorithm Based on 3D Vision Selectivity and Its Application in Scene Segmentation

“…Breakthroughs in 3D sensor technology [1] that overcome the precision bottleneck of point cloud collection can capture accurate point cloud datasets, providing a data foundation for the rapid advancement of 3D point cloud data processing algorithms. These advancements have achieved good research and application results in fields such as drone control [2], autonomous driving [3], augmented reality [4], and medical image processing [5].…”

“…However, the structuring algorithms for 3D point cloud data have the following shortcomings: (1) Structuring of 3D space results in overlap of some data, compromising the purity and completeness of the original data points. (2) The process of structuring 3D space increases the complexity of the algorithms and reduces their performance. (3) Polarized structuring (such as the view method) loses three-dimensional topological structure information, affecting the recognition performance of the algorithms.…”