Controlling and manning robots from a remote location is difficult because of the limitations one faces in perception and available degrees of actuation. Although humans can become skilled teleoperators, the amount of training time required to acquire such skills is typically very high. In this paper, we propose a novel solution (named Autocomplete) to aid novice teleoperators in manning robots adroitly. At the input side, Autocomplete relies on machine learning to detect and categorize human inputs as one from a group of motion primitives. Once a desired motion is recognized, at the actuation side an automated command replaces the human input in performing the desired action. So far, Autocomplete can recognize and synthesize lines, arcs, full circles, 3-D helices, and sine trajectories. Autocomplete was tested in simulation on the teleoperation of an unmanned aerial vehicle, and results demonstrate the advantages of the proposed solution versus manual steering.

In this work, we are proposing an intuitive Augmented SLAM method (A-SLAM) that allows the user to interact, in real-time, with a robot running SLAM to correct for pose and map errors. We built an AR application that works on HoloLens and allows the operator to view the robot's map superposed on the physical environment and edit it. Through map editing, the operator can account for errors affecting real environment's representation by adding navigation-forbidden areas to the map in addition to the ability to correct errors affecting the localization. The proposed system allows the operator to edit the robot's pose (based on SLAM request) and can be extended to sending navigation goals to the robot, viewing the planned path to evaluate it before execution, and teleoperating the robot. The proposed solution could be applied on any 2D-based SLAM algorithm and can easily be extended to 3D SLAM techniques. We validated our system through experimentation on pose correction and map editing. Experiments demonstrated that through A-SLAM, SLAM runtime is cut to half, post-processing of maps is totally eliminated, and high quality occupancy grid maps could be achieved with minimal added computational and hardware costs.

In this paper, we are proposing a collaborative SLAM system between a team of three heterogeneous agents: a robot, a human operator, and an augmented reality head mounted display (ARHMD). The system allows for online editing of a map produced by a robot running SLAM. Through hand gestures, the user can edit, in real time, the robot map that is augmented on top of the physical environment. Moreover, the proposed system leverages the built-in SLAM capabilities of the AR-HMD to correct the robot's map and map areas that are not yet discovered by the robot. Our method aims to combine the unique and complementary capabilities of each of the three different agents to produce the maximum possible mapping accuracy in the minimum amount of time. The proposed system is implemented on ROS and Unity. Experiments performed demonstrate the considerably superior SLAM outputs in terms of reducing mapping time, eliminating maps post-processing, and increasing mapping accuracy.

In this paper we develop a real-time human augmented mapping system. This approach replaces the traditional offline post processing of maps by a user-friendly system allowing for online editing capabilities. A wide number of applications that acquire accurate mapping of the environment could benefit from such a solution. The proposed framework consists of two main parts: 2D map building using LIDAR, encoders, and IMU; and a user interface for human map augmentation. The first part is built over Gmapping ROS package, while the second is developed in Unity software. Realworld experiments validated the ability of our system to correct for sensor noise and various mapping errors, thus increasing the accuracy of the obtained maps without additional computational costs.