Digital cameras are being widely used in robotics in 3D scene reconstruction, simultaneous localization and mapping (SLAM), and other applications, which often require real-time video stream from multiple cameras. In a case of a mobile robotic system with limited hardware capabilities performance issues may arise. In this work we present a real-time video streaming robot operating system (ROS) package for our crawler-type mobile robot Servosila Engineer, which has four on-board cameras. Compared to OpenCV based solution our package demonstrates less resource consumption.

Robotic warehouse management is a promising field of research with a high practical applicability. Typically, warehouses are strictly organized and controlled environments with a high potential for rapid robot deployments. To provide system robustness every robotic deployment should be tested in advance using simulations. Such virtual testing environment should correspond well with a real world warehouse environment and be consistent with international and national standards. In this paper, we present a warehouse Gazebo simulation that was constructed in accordance with Government Standards of Russian Federation and international standards. We implemented a simplified Radio Frequency IDentification (RFID) transmitter-receiver mechanism within the Gazebo simulator and tested it in a virtual warehouse environment using a heterogeneous team of TIAGo Base mobile robot and PX4-based UAV. Our constructed virtual warehouse environment and RFID mechanism models are open-source for academic community use.

robo-server.uacj.mx Unmanned Aerial Vehicle (UAV) is a flying robot that acts without a constant human pilot involvement. UAVs are applied in military and civilian areas, in search and rescue operations, 3D mapping, simultaneous localization and mapping (SLAM) and other tasks. SLAM approaches are based on various sensors usage including lidars and cameras. Visual SLAM approaches rely on visual sensing systems and successfully operate within GPS-denied environments. Further, applying several UAVs allows for complex tasks that cannot be handled by a single robot, minimizes exploration time and adds a security level for a case of a single robot failure. This paper presents a comparison of two most applicable vision-based collaborative monocular SLAM methods in Robot operating system, CORB-SLAM and CCM-SLAM, that run on a pair of UAVs. The evaluation is performed on preassembled datasets that correspond to a virtual environment in the Gazebo simulator. The error estimation in virtual experiments demonstrated that CCM-SLAM has a higher global localization accuracy than CORB-SLAM.