CUDA-Based Computation for Visual Odometry

Shen Ho Liu; Chen Chien Hsu; Wei Yen Wang; Cheng Hung Lin

doi:10.1109/GCCE.2018.8574869

CUDA-Based Computation for Visual Odometry

Shen Ho Liu, Chen Chien Hsu, Wei Yen Wang, Cheng Hung Lin

Department of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

An enhanced visual odometry (VO) system is proposed to improve the accuracy of pose estimation based on a corrected model, and the matching algorithm is implemented on graphical processing units (GPUs) so that the computation can be accelerated in parallel and in real-time using the compute unified device architecture (CUDA) programming model. To evaluate the performance of the proposed approach, an ASUS Xtion 3D camera, laptop, and NVIDIA TX2 are employed to conduct extensive experiments. The experimental results show that compared with the traditional VO algorithm, the proposed approach gives better results over the traditional VO algorithm.

Original language	English
Title of host publication	2018 IEEE 7th Global Conference on Consumer Electronics, GCCE 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	55-56
Number of pages	2
ISBN (Electronic)	9781538663097
DOIs	https://doi.org/10.1109/GCCE.2018.8574869
Publication status	Published - 2018 Dec 12
Event	7th IEEE Global Conference on Consumer Electronics, GCCE 2018 - Nara, Japan Duration: 2018 Oct 9 → 2018 Oct 12

Publication series

Name	2018 IEEE 7th Global Conference on Consumer Electronics, GCCE 2018

Other

Other	7th IEEE Global Conference on Consumer Electronics, GCCE 2018
Country/Territory	Japan
City	Nara
Period	2018/10/09 → 2018/10/12

Keywords

CUDA
GPU
Visual odometry

ASJC Scopus subject areas

Computer Networks and Communications
Electrical and Electronic Engineering
Safety, Risk, Reliability and Quality
Instrumentation

Access to Document

10.1109/GCCE.2018.8574869

Cite this

CUDA-Based Computation for Visual Odometry. / Liu, Shen Ho; Hsu, Chen Chien ; Wang, Wei Yen et al.
2018 IEEE 7th Global Conference on Consumer Electronics, GCCE 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 55-56 8574869 (2018 IEEE 7th Global Conference on Consumer Electronics, GCCE 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Liu, SH, Hsu, CC , Wang, WY & Lin, CH 2018, CUDA-Based Computation for Visual Odometry. in 2018 IEEE 7th Global Conference on Consumer Electronics, GCCE 2018., 8574869, 2018 IEEE 7th Global Conference on Consumer Electronics, GCCE 2018, Institute of Electrical and Electronics Engineers Inc., pp. 55-56, 7th IEEE Global Conference on Consumer Electronics, GCCE 2018, Nara, Japan, 2018/10/09. https://doi.org/10.1109/GCCE.2018.8574869

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title = "CUDA-Based Computation for Visual Odometry",

abstract = "An enhanced visual odometry (VO) system is proposed to improve the accuracy of pose estimation based on a corrected model, and the matching algorithm is implemented on graphical processing units (GPUs) so that the computation can be accelerated in parallel and in real-time using the compute unified device architecture (CUDA) programming model. To evaluate the performance of the proposed approach, an ASUS Xtion 3D camera, laptop, and NVIDIA TX2 are employed to conduct extensive experiments. The experimental results show that compared with the traditional VO algorithm, the proposed approach gives better results over the traditional VO algorithm.",

keywords = "CUDA, GPU, Visual odometry",

author = "Liu, {Shen Ho} and Hsu, {Chen Chien} and Wang, {Wei Yen} and Lin, {Cheng Hung}",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 7th IEEE Global Conference on Consumer Electronics, GCCE 2018 ; Conference date: 09-10-2018 Through 12-10-2018",

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doi = "10.1109/GCCE.2018.8574869",

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N2 - An enhanced visual odometry (VO) system is proposed to improve the accuracy of pose estimation based on a corrected model, and the matching algorithm is implemented on graphical processing units (GPUs) so that the computation can be accelerated in parallel and in real-time using the compute unified device architecture (CUDA) programming model. To evaluate the performance of the proposed approach, an ASUS Xtion 3D camera, laptop, and NVIDIA TX2 are employed to conduct extensive experiments. The experimental results show that compared with the traditional VO algorithm, the proposed approach gives better results over the traditional VO algorithm.

AB - An enhanced visual odometry (VO) system is proposed to improve the accuracy of pose estimation based on a corrected model, and the matching algorithm is implemented on graphical processing units (GPUs) so that the computation can be accelerated in parallel and in real-time using the compute unified device architecture (CUDA) programming model. To evaluate the performance of the proposed approach, an ASUS Xtion 3D camera, laptop, and NVIDIA TX2 are employed to conduct extensive experiments. The experimental results show that compared with the traditional VO algorithm, the proposed approach gives better results over the traditional VO algorithm.

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ER -

CUDA-Based Computation for Visual Odometry

Abstract

Publication series

Other

Keywords

ASJC Scopus subject areas

Access to Document

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