Collaborative Mapping with Pose Uncertainties using different Radio Frequencies and Communication Modules Many robotic applications, especially exploration scenarios, benefit from deploying multiple collaborating robots with the aim of parallelizing and therefore accelerating the involved task. One critical part of a multirobot system is its communication system. Depending on the application scenario, highbandwidth wireless connections, such as WiFi, may not always be available and suffer from a limited communication range. On the other hand, lowbandwith systems require the application itself to deal with limited information exchange. In this work, we present a novel approach for collaborative mapping using mixtures of occupancy and NDT maps (called ONDT), which provide detailed information at low resolutions and in which the pose uncertainty can be encoded efficiently. Further, we compare the applicability of three different radio frequency modules operating at different frequencies in real world experiments with five robots at large distances.

Collaborative Human Augmented SLAM 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 builtin SLAM capabilities of the ARHMD to correct the robotxe2x80x99s 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 postprocessing, and increasing mapping accuracy.