@inproceedings{92fc3d09194d4fe2bb6dc034a3b9efd4,
title = "An adaptive strategy for monocular visual odometry",
abstract = "Monocular Visual odometry is an important technique in mobile robot localization and navigation. This paper first empirically studies two kinds of commonly used monocular visual odometry (MVO): descriptor-based methods and optical flow based methods. Six representative scenes are extracted from KITTI and Karlsruhe datasets. Ten MVO algorithms are evaluated in terms of real-time performance and trajectory accuracy. Experimental results show that different MVO algorithms show different performance in different scenarios. Furthermore, an adaptive visual odometry(AVO) strategy is proposed on the basis of the experiment results. The changing environment is detected and the most suitable MVO algorithm is chosen dynamically according to a cost function. The experimental results show that the AVO method can obtain higher trajectory accuracy and better real-time performance.",
keywords = "Adaptive MVO, Monocular visual odometry, Pose estimation",
author = "Shengguang Xie and Mengxiang Lin and Meng Hang and Rong Ding",
note = "Publisher Copyright: {\textcopyright} 2017 Association for Computing Machinery.; 2017 International Conference on Robotics and Artificial Intelligence, ICRAI 2017 ; Conference date: 29-12-2017 Through 31-12-2017",
year = "2017",
month = dec,
day = "29",
doi = "10.1145/3175603.3175619",
language = "英语",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery ",
pages = "46--50",
booktitle = "Proceedings of 2017 International Conference on Robotics and Artificial Intelligence, ICRAI 2017",
address = "美国",
}