Real time 3D localization and mapping for USAR robotic application

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.