State Estimation and Localization for ROV-Based Reactor Pressure Vessel Inspection

Abstract

A vision-based extended Kalman filter is proposed to estimate the state of a remotely operated vehicle (ROV) used for inspection of a nuclear reactor pressure vessel. The state estimation framework employs an overhead, pan-tilt-zoom (PTZ) camera as the primary sensing modality. In addition to the camera state, a map of the nuclear reactor vessel is also estimated from a prior. We conduct experiments to validate the framework in terms of accuracy and robustness to environmental image degradation due to speckling and color attenuation. Subscale mockup experiments highlight estimate consistency as compared to ground truth despite visually degraded operated conditions. Full-scale platform experiments are conducted using the actual inspection system in a dry setting. In this case, the ROV achieves a lower state uncertainty as compared to subscale mockup evaluation. For both subscale and full-scale experiments, the state uncertainty was robust to environmental image degradation effects.