ROV localization in the nuclear reactor pressure vessel using LSTM and an improved adaptive Kalman Filter

In this paper, a remotely operated vehicle (ROV) localization algorithm for nuclear reactor pressure vessels is introduced. Traditional machine vision localization suffers from visual blind zones. To address this issue, a long short-term memory (LSTM) network was employed to obtain the ROV's position. To further enhance the accuracy, an improved Sage-Husa adaptive Kalman Filter (SHAKF) was applied to eliminate cumulative errors from LSTM. Finally, upper-diagonal decomposition and confidence checks were used to enhance the traditional SHAKF, addressing issues associated with the non-positive definite error covariance matrix and sensor limitations. Simulated and experimental results demonstrated that the LSTM network significantly reduced the cumulative error from trajectory projection localization. The root mean square error of the localization results was reduced from 51.776 to 12.808 mm due to the improvements made to the traditional SHAKF. Additionally, the improved SHAKF effectively minimized the cumulative LSTM localization error.