@inproceedings{1e4e38d3baf44a77861a3647dff050af,
title = "Enhanced visual odometry algorithm based on elite selection method and voting system",
abstract = "In this paper, we address the problems of camera pose estimation accuracy and runtime efficiency by incorporating an elite selection method and a voting system to a conventional visual odometry (VO) method, called the 'enhanced VO algorithm'. The use of elite selection method improves the efficiency of perspective-3-point (P3P) algorithm by only employing an elite subset of landmarks to estimate the camera pose. The proposed voting system, on the other hand, provides reliable consensus set derived from random sample consensus (RANSAC) algorithm such that accuracy of camera pose estimations can be increased. To verify the performances of the proposed approach, we conducted various experiments using a Kinect RGB-D sensor, and the results show that the proposed VO system performs well in terms of not only estimation accuracy but also computational time.",
keywords = "Kinect RGB-D sensor, Perspective-3-point, RANSAC, SURF, Visual odometry",
author = "Hao Shen and Hsu, {Chen Chien} and Wang, {Wei Yen} and Wang, {Yin Tien}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 7th IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2017 ; Conference date: 03-09-2017 Through 06-09-2017",
year = "2017",
month = dec,
day = "14",
doi = "10.1109/ICCE-Berlin.2017.8210602",
language = "English",
series = "IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin",
publisher = "IEEE Computer Society",
pages = "99--100",
booktitle = "2017 IEEE 7th International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2017",
}