TY - GEN
T1 - CUDA-Based Computation for Visual Odometry
AU - Liu, Shen Ho
AU - Hsu, Chen Chien
AU - Wang, Wei Yen
AU - Lin, Cheng Hung
N1 - Funding Information:
This research is supported by the "Aim for the Top University Project" of National Taiwan Normal University (NTNU), sponsored by the Ministry of Education, Taiwan, and Ministry of Science and Technology, Taiwan, under Grants no. MOST 107-2634-F-003-002 and MOST 107-2634-F-003-001.
Funding Information:
This research is supported by the “Aim for the Top University Project” of National Taiwan Normal University (NTNU), sponsored by the Ministry of Education, Taiwan, and Ministry of Science and Technology, Taiwan, under Grants no. MOST 107-2634-F-003-002 and MOST 107-2634-F-003-001.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/12/12
Y1 - 2018/12/12
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.
KW - CUDA
KW - GPU
KW - Visual odometry
UR - http://www.scopus.com/inward/record.url?scp=85060315763&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85060315763&partnerID=8YFLogxK
U2 - 10.1109/GCCE.2018.8574869
DO - 10.1109/GCCE.2018.8574869
M3 - Conference contribution
AN - SCOPUS:85060315763
T3 - 2018 IEEE 7th Global Conference on Consumer Electronics, GCCE 2018
SP - 55
EP - 56
BT - 2018 IEEE 7th Global Conference on Consumer Electronics, GCCE 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th IEEE Global Conference on Consumer Electronics, GCCE 2018
Y2 - 9 October 2018 through 12 October 2018
ER -