@inproceedings{50d3d18ddbc14f358564f0d80d7166e2,
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",
year = "2018",
month = dec,
day = "12",
doi = "10.1109/GCCE.2018.8574869",
language = "English",
series = "2018 IEEE 7th Global Conference on Consumer Electronics, GCCE 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "55--56",
booktitle = "2018 IEEE 7th Global Conference on Consumer Electronics, GCCE 2018",
}