Efficacy of statistical model-based pose estimation of rigid objects with corresponding CAD models using commodity depth sensors

Since the emergence of the commodity-priced, small-sized Microsoft Kinect™ sensor, 3D object pose estimation has become prevalent in many applications across a wide variety of disciplines. However, because most current methods require a hard assignment between a measurement point cloud and a given CAD model point cloud for alignment, accurate pose estimation is limited to a small range of sensor noise and resolution, and CAD model precision. This paper therefore presents a MLE algorithm that achieves a statistically optimal global maximum likelihood on the surface of the continuous 6D pose domain by soft assigning all measurement points to all model points. The accuracy in estimation of orientation and position of the MLE algorithm is then compared to a variant of the ICP method that accounts for an anisotropie Gaussian measurement noise distribution. It is finally shown through a series of simulated measurement point clouds and depth images that MLE outperforms the ICP variant, and achieves a monotonie increase in performance with an increase in either points on target or CAD model precision.