Robust HandEye Calibration via Iteratively Reweighted RankConstrained SemiDefinite Programming This paper addresses the problem of estimating the Euclidean transformation relating two rigidly attached reference frames from motion measurements: a problem that is commonly referred to as the HandEye calibration. The motion measurements are often affected by synchronization issues and hardware malfunctions. When using pose sensors, such as Electromagnetic or Inertial Measurement Units, the measurements are often less reliable than those obtained by a traditional robotlink and camera pair. Corrupt measurements, whether due to disturbances, large synchronization mismatches or malfunctions, are considered outliers that ought to be filtered out of the estimation process. While this may be achieved nondeterministically by using Random Sample Consensus, we propose a deterministic, robust and accurate method for solving the HandEye calibration problem despite the presence of large amounts of outliers and high levels of measurement noise. The proposed method is based on a reformulation of this estimation problem as a rankconstrained semidefinite programming problem allowing for robustness to be enforced via an iteratively reweighted optimization approach.

A Novel Robust Approach for CorrespondenceFree Extrinsic Calibration Extrinsic calibration is a necessary step when using heterogeneous sensors for robotics applications. Most existing methods work under the assumption that the prior data correspondence is known. Considering data loss and false measurements, the correspondence may not be accessible in practice. To solve this problem without knowing the correspondence, several probabilistic methods have been proposed. However, an implicit restriction on input data limits their application. Therefore, in this paper, we propose a more stable correspondencefree method with two improvements that can relax the restrictions on inputs and improve the calibration accuracy. The first improvement finds consistent sets from raw inputs using screw invariants, which significantly improve the robustness in case of outliers and data loss. A new optimization method on matrix Lie group is proposed as the second improvement, which demonstrates better accuracy. The experimental results on both numerical and real data show the superiority and robustness of the proposed method.