Abstract
Due to space and energy restrictions, lightweight autonomous underwater vehicles (AUVs) are usually fitted with low-power processing units, which limits the ability to run demanding applications in real time during the mission. However, several robotic perception tasks reveal a parallel nature, where the same processing routine is applied for multiple independent inputs. In such cases, leveraging parallel execution by offloading tasks to a GPU can greatly enhance processing speed. This article presents a collection of generic matrix manipulation kernels, which can be combined to develop parallelized perception applications. Taking advantage of those building blocks, we report a parallel implementation for the 3DupIC algorithm-a probabilistic scan matching method for sonar scan registration. Tests demonstrate the algorithm's real-time performance, enabling 3D sonar scan matching to be executed in real time onboard the EVA AUV.