Programming human-like point-to-point approaching movement by demonstrations with Large-Scale Direct Monocular SLAM

Abstract

With the development of robotic technologies over last decades, the robotic mapping or the simultaneous localization and mapping (SLAM) has got matured correspondingly. The Large-Scale Direct Monocular SLAM (LSD-SLAM) is among the most promising methods in SLAM, which not only locally tracks the motion of the camera, but allows to build consistent and large-scale maps of the environment. In this paper, LSD-SLAM is implemented to model the surrounding environment of a three degrees-of-freedom robot manipulator. All the objects in the robot workspace have been recognized and located. And the robot will take the extracted information as visual input to perform a point-to-point approaching task A novel motion planning technique, programming by demonstrations (PbD), is used to transfer human approaching skills to the robot. Gaussian Mixture Models (GMM) and Gaussian Mixture Regression (GMR) are used to encode human demonstrations with a first-order differential dynamical systems model. Experimental results have verified the effectiveness of the proposed methods.