Accelerating the Sweep3D for a Graphic Processor Unit

Gong, Chunye; Liu, Jie; Chen, Haitao; Xie, Jing; Zhang, Gong

doi:10.3745/jips.2011.7.1.063

Cited by 8 publications

(3 citation statements)

References 10 publications

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“…In this paper, we propose a real-time traversable ground surface segmentation and intuitive representation system. To speed up the computation of the proposed methods, we used graphics processing unit (GPU) programming to implement the segmentation and modeling processes for large-scale datasets in parallel [10].…”

Section: Introductionmentioning

confidence: 99%

Traversable Ground Surface Segmentation and Modeling for Real-Time Mobile Mapping

Song

Cho

et al. 2014

International Journal of Distributed Sensor Networks

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Remote vehicle operator must quickly decide on the motion and path. Thus, rapid and intuitive feedback of the real environment is vital for effective control. This paper presents a real-time traversable ground surface segmentation and intuitive representation system for remote operation of mobile robot. Firstly, a terrain model using voxel-based flag map is proposed for incrementally registering large-scale point clouds in real time. Subsequently, a ground segmentation method with Gibbs-Markov random field (Gibbs-MRF) model is applied to detect ground data in the reconstructed terrain. Finally, we generate a texture mesh for ground surface representation by mapping the triangles in the terrain mesh onto the captured video images. To speed up the computation, we program a graphics processing unit (GPU) to implement the proposed system for large-scale datasets in parallel. Our proposed methods were tested in an outdoor environment. The results show that ground data is segmented effectively and the ground surface is represented intuitively.

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Section: Introductionmentioning

confidence: 99%

Traversable Ground Surface Segmentation and Modeling for Real-Time Mobile Mapping

Song

Cho

et al. 2014

International Journal of Distributed Sensor Networks

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“…In recent times, with the rapid development of graphics cards, a graphics processing unit (GPU) with a highly parallel many-core architecture has been widely used in 2D image processing, 3D data analysis, visualization, and other fields [ 8 , 9 ]. GPU programming can be used to realize a high-speed and high-quality large-scale terrain reconstruction system [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Terrain Storage Generation from Multiple Sensors towards Mobile Robot Operation Interface

Song

Cho

et al. 2014

The Scientific World Journal

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A mobile robot mounted with multiple sensors is used to rapidly collect 3D point clouds and video images so as to allow accurate terrain modeling. In this study, we develop a real-time terrain storage generation and representation system including a nonground point database (PDB), ground mesh database (MDB), and texture database (TDB). A voxel-based flag map is proposed for incrementally registering large-scale point clouds in a terrain model in real time. We quantize the 3D point clouds into 3D grids of the flag map as a comparative table in order to remove the redundant points. We integrate the large-scale 3D point clouds into a nonground PDB and a node-based terrain mesh using the CPU. Subsequently, we program a graphics processing unit (GPU) to generate the TDB by mapping the triangles in the terrain mesh onto the captured video images. Finally, we produce a nonground voxel map and a ground textured mesh as a terrain reconstruction result. Our proposed methods were tested in an outdoor environment. Our results show that the proposed system was able to rapidly generate terrain storage and provide high resolution terrain representation for mobile mapping services and a graphical user interface between remote operators and mobile robots.

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“…General-Purpose GPU is a highly parallel, multithreaded, many-core processor with a very high computational power and memory bandwidth [3]. GPU architecture is designed for optimization of massively parallel computing, because of this architectural difference, a GPU is in general more advantageous for large-scale parallel data processing applications than general-purpose CPUs [4], and the high performance computing community leveraging a GPU can yield performance increases of several orders of magnitude [5,6]. High-performance computing with GPUs is called GPU computing [7].…”

Section: Introduction Introduction Introduction Introductionmentioning

confidence: 99%

Markov Decision Process Parallel Value Iteration Algorithm On GPU

Chen¹,

Lu²

2013

Proceedings of the 2013 International Conference on Information Science and Computer Applications (ISCA 2013)

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This paper defines an Out Of Play model based on Markov Decision Process. The best path for playing can be found and recommended by using this model, and a value iteration algorithm of Markov Decision Process is used to implement the model. In this paper, the implementation of this model with CPU is presented. And then, in order to improve the performance of the value iteration algorithm, a parallel value iteration algorithm on GPU is designed and showed. For the calculation of a large amount of data, the experimental results show that the parallel value iteration algorithm on GPU improves performance far more than that of the serial value iteration algorithm on CPU.

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Accelerating the Sweep3D for a Graphic Processor Unit

Cited by 8 publications

References 10 publications

Traversable Ground Surface Segmentation and Modeling for Real-Time Mobile Mapping

Traversable Ground Surface Segmentation and Modeling for Real-Time Mobile Mapping

Real-Time Terrain Storage Generation from Multiple Sensors towards Mobile Robot Operation Interface

Markov Decision Process Parallel Value Iteration Algorithm On GPU

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