Purpose -The purpose of this paper is to demonstrate a real time 3D localization and mapping approach for the USAR (Urban Search and Rescue) robotic application, focusing on the performance and the accuracy of the General-purpose computing on graphics processing units (GPGPU)-based iterative closest point (ICP) 3D data registration implemented using modern GPGPU with FERMI architecture. Design/methodology/approach -The authors put all the ICP computation into GPU, and performed the experiments with registration up to 106 data points. The main goal of the research was to provide a method for real-time data registration performed by a mobile robot equipped with commercially available laser measurement system 3D. The main contribution of the paper is a new GPGPU based ICP implementation with regular grid decomposition. It guarantees high accuracy as equivalent CPU based ICP implementation with better performance. Findings -The authors have shown an empirical analysis of the tuning of GPUICP parameters for obtaining much better performance (acceptable level of the variance of the computing time) with minimal lost of accuracy. Loop closing method is added and demonstrates satisfactory results of 3D localization and mapping in urban environments. This work can help in building the USAR mobile robotic applications that process 3D cloud of points in real time.Practical implications -This work can help in developing real time mapping for USAR robotic applications. Originality/value -The paper proposes a new method for nearest neighbor search that guarantees better performance with minimal loss of accuracy. The variance of computational time is much less than SoA.

The paper concerns the results related with GPGPU computing applied for mobile robotics applications. The scalable implementation of the point to point and point to plane 3D data registration methods with an improvement based on regular grid decomposition is shown. 3D data is delivered by mobile robot equipped with 3D laser measurement system for INDOOR environments. Presented empirical analysis of the implementation shows the On-Line computation capability using modern graphic processor unit NVIDIA GF 580. In the paper the discussion concerning the comparison between these two methods is given. It will be shown why the point to plain ICP implementation can achieve better performance than the point to point approach. We show parallel vector computation that is used for semantic objects identifications and for loop closing detection.

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In this paper, a Training and Support system for Search and Rescue operations is described. The system is a component of the ICARUS project (http://www.fp7-icarus.eu) which has a goal to develop sensor, robotic and communication technologies for Human Search And Rescue teams. The support system for planning and managing complex SAR operations is implemented as a command and control component that integrates different sources of spatial information, such as maps of the affected area, satellite images and sensor data coming from the unmanned robots, in order to provide a situation snapshot to the rescue team who will make the necessary decisions. Support issues will include planning of frequency resources needed for given areas, prediction of coverage conditions, location of fixed communication relays, etc. The training system is developed for the ICARUS operators controlling UGVs (Unmanned Ground Vehicles), UAVs (Unmanned Aerial Vehicles) and USVs (Unmanned Surface Vehicles) from a unified Remote Control Station (RC2). The Training and Support system is implemented in SaaS model (Software as a Service). Therefore, its functionality is available over the Ethernet. SAR ICARUS teams from different countries can be trained simultaneously on a shared virtual stage. In this paper we will show the multi-robot 3D mapping component (aerial vehicle and ground vehicles). We will demonstrate that these 3D maps can be used for Training purpose. Finally we demonstrate current approach for ICARUS Urban SAR (USAR) and Marine SAR (MSAR) operation training.