Robust pose estimation with the stereoscopic camera in harsh environment

Remote teleoperation of robotic manipulators requires a robust machine vision system in order to perform accurate movements in the navigated environment. Even though a 3D CAD model can be available, the dimensions and poses of its compo-nents are subject to changes due to extreme conditions. Integration of stereoscopic camera into the control chain enables a more precise object detection, pose-estimation and tracking. However, the conventional stereoscopic pose-estimation methods still lack robustness and accuracy in the presence of harsh-environment conditions, such as high-dose of radiation, deficient illumination, shiny metallic surfaces, etc. In this paper we investigate capability of a specifically tuned Iterative Closest Point (ICP) algorithm to operate in the aforementioned environments and suggest algorithmic improvements. We demonstrate that proposed algorithm outperforms current state-of-the-art methods in both robustness and accuracy. The experiments are performed with a real robotic manipulator prototype and a stereoscopic machine vision system.