Efficient Training Data Collection for Distance Sensor Arrays Through Data Correction and Augmentation Approaches

Abstract

Several machine learning (ML)-based measurement systems have been proposed to estimate difficult-to-measure quantities from the values of distance sensor arrays. However, variations in sensor output characteristics (OCs) can lead to degradation in the estimation accuracy when transferring training data acquired from the original acquisition sensors to new target sensors. Moreover, acquiring training data from target sensors is time and labor intensive. We propose two methods to convert previously collected training data to reflect different OCs, enabling their repeated use. For evaluation, we use a device that estimates the relative position and orientation of vehicles based on the values of distance sensor arrays. The correction approach for the training data based on the OC data reduces the root-mean-square error (RMSE) by up to 23% compared with transferring training data. The augmentation approach transforms the training data into data that include different OCs using a mapping function constructed from a small batch of training data. Furthermore, a method for collecting a small batch of training data to achieve a higher OC conversion accuracy is demonstrated. The RMSE is reduced by up to 58% by the proposed method compared with transferring training data. The results of this study demonstrate the feasibility of the practical applications of ML-based measurement systems using distance sensor arrays, which may facilitate the development of simple and fast calibration methods.