In this paper, we describe a method for recognizing objects in the form of point clouds acquired with a laser scanner. This method is fully implemented on GPU and uses bio-inspired metaheuristics, namely PSO or DE, to evolve the rigid transformation that best aligns some References extracted from a dataset to the target point cloud.We compare the performance of our method with an established method based on Fast Point Feature Histograms (FPFH). The results prove that FPFH is more reliable under simple and controlled situations, but PSO and DE are more robust with respect to common problems as noise or occlusions.
GPU-based point cloud recognition using evolutionary algorithms / Ugolotti, Roberto; Micconi, Giorgio; Aleotti, Jacopo; Cagnoni, Stefano. - 8602:(2014), pp. 489-500. (Intervento presentato al convegno 17th European Conference on Applications of Evolutionary Computation, EvoApplications 2014 tenutosi a Granada, Spain nel April 2014) [10.1007/978-3-662-45523-4_40].
GPU-based point cloud recognition using evolutionary algorithms
UGOLOTTI, Roberto;MICCONI, GIORGIO;ALEOTTI, Jacopo;CAGNONI, Stefano
2014-01-01
Abstract
In this paper, we describe a method for recognizing objects in the form of point clouds acquired with a laser scanner. This method is fully implemented on GPU and uses bio-inspired metaheuristics, namely PSO or DE, to evolve the rigid transformation that best aligns some References extracted from a dataset to the target point cloud.We compare the performance of our method with an established method based on Fast Point Feature Histograms (FPFH). The results prove that FPFH is more reliable under simple and controlled situations, but PSO and DE are more robust with respect to common problems as noise or occlusions.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
