Building an Adaptive Machine Learning Object-Positioning System in a Monocular Vision Environment

Abstract

An adaptive machine-learning object-localization system was proposed in this study and applied to an automated carrier robot equipped with monocular vision, and the problem of object-distance localization in vision was improved using machine learning. When executing a project involving small robots, the calculation of the target object by the object-distance algorithm depends on the system-defined parameters, such as camera height, field of view, and inclination angle. When the carrier encounters an inclination that causes changes in the camera's external parameters, the parameters defined in the system differ from those under real conditions, causing severe errors in calculation results. This system combined a triaxial sensor and machine-learning regression to identify the functional relationship between the output signal of the accelerometer and the actual carrier attitude. The regression network model was trained by recording several sets of signal features and carrier angles as the data set. During object-distance calculation, the adaptive machine-learning object-localization system predicted the current carrier angle by using the output of the accelerometer and the trained regression network model to update the algorithm's angle parameters. The experimental results indicated that the system possessed stability and accuracy in calculating the distance of the target object when the carrier angle changed.