DOPESLAM: A high-precision ROS-based semantic SLAM system

Abstract Recent advancements in deep learning techniques have accelerated the growth of robotic vision systems. One way this technology can be applied is to use a mobile robot to automatically generate a 3D map and identify objects within it. This paper explores a solution to this problem by combining Deep Object Pose Estimation (DOPE) with Real-Time Appearance-Based Mapping (RTAB-Map) through means of loose-coupled parallel fusion. DOPE’s abilities are enhanced by leveraging its belief map system to filter uncertain key points which increases precision so only the best object labels end up on the map. Additionally, DOPE’s pipeline is modified to enable shape-based object recognition using depth maps so it can identify objects in complete darkness. Three experiments are performed to find the ideal training dataset, quantify the increased precision, and to evaluate the overall performance of the system. The results show the proposed solution outperforms existing methods in most intended scenarios such as in unilluminated scenes.