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GPU-Mapping: Robotic Map Building with Graphical Multiprocessors.

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Title: GPU-Mapping: Robotic Map Building with Graphical Multiprocessors.
Authors: Rodriguez-Losada, Diego1, San Segundo, Pablo1, Hernando, Miguel1, de la Puente, Paloma1, Valero-Gomez, Alberto1
Source: IEEE Robotics & Automation Magazine. Jun2013, Vol. 20 Issue 2, p40-51. 12p.
Subjects: Robotics, Cartography automation, Algorithms, Computer software development, Least squares
Abstract: This article provides a broad perspective of the potential applicability of graphical processing units (GPUs) computing power in robotics, specifically in the well-known problem of two-dimensional (2-D) robotic mapping. There are three possible ways of exploiting these massively parallel devices: 1) parallelizing existing algorithms, 2) integrating already existing parallelized general purpose software, and 3) use of its high-computational capabilities in the inception of new algorithms. This article presents examples for all three options: parallelizing a popular implementation of the gridmapping algorithm, using a GPU open-source linear sparse system solver to address the problem of linear least squares graph minimization, and developing a novel method that can be efficiently parallelized and executed in a GPU for handling overlapping grid maps in a mapping with local maps algorithm. Large speedups are shown in the experiments, highlighting the importance of this technology in robotic software development in the near future, as is already the case in many other areas. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:This article provides a broad perspective of the potential applicability of graphical processing units (GPUs) computing power in robotics, specifically in the well-known problem of two-dimensional (2-D) robotic mapping. There are three possible ways of exploiting these massively parallel devices: 1) parallelizing existing algorithms, 2) integrating already existing parallelized general purpose software, and 3) use of its high-computational capabilities in the inception of new algorithms. This article presents examples for all three options: parallelizing a popular implementation of the gridmapping algorithm, using a GPU open-source linear sparse system solver to address the problem of linear least squares graph minimization, and developing a novel method that can be efficiently parallelized and executed in a GPU for handling overlapping grid maps in a mapping with local maps algorithm. Large speedups are shown in the experiments, highlighting the importance of this technology in robotic software development in the near future, as is already the case in many other areas. [ABSTRACT FROM AUTHOR]
ISSN:10709932
DOI:10.1109/MRA.2012.2220503