6D SLAM with GPGPU computation

Abstract

The main goal was to improve a state of the art 6D SLAM algorithm with a new GPGPU-based implementation of da- ta registration module. Data registration is based on ICP (Iterative Closest Point) algorithm that is fully implemented in the GPU with NVIDIA FERMI architecture. In our research we focus on mobile robot inspection intervention systems applicable in hazardous envi- ronments. The goal is to deliver a complete system capable of being used in real life. In this paper we demonstrate our achievements in the field of on line robot localization and mapping. We demon- strated an experiment in real large environment. We compared two strategies of data alignment - simple ICP and ICP using so called meta scan. Abstract: The main goal was to improve a state of the art 6D SLAM algorithm with a new GPGPU-based implementation of da- ta registration module. Data registration is based on ICP (Iterative Closest Point) algorithm that is fully implemented in the GPU with NVIDIA FERMI architecture. In our research we focus on mobile robot inspection intervention systems applicable in hazardous envi- ronments. The goal is to deliver a complete system capable of being used in real life. In this paper we demonstrate our achievements in the field of on line robot localization and mapping. We demon- strated an experiment in real large environment. We compared two strategies of data alignment - simple ICP and ICP using so called meta scan.