@inproceedings{2f8ab32c67cf4c40909505f8d85c207f,
title = "Path Planner for Keyframe-based Visual Autonomous Navigation",
abstract = "Visual navigation systems have received much attention in recent years. Such systems generate keyframes storing the sensor information that provides a way to correct the robot pose. However, the conventional gradient path does not generate a path that tracks the keyframes since it is based only on the costs regarding the environment. In this study, we propose a keyframe vector-based path planner (KVPP) that is more suitable for visual navigation systems as this path follows more keyframes in the map to increase the chance of keyframe-based pose correction during autonomous driving. This KVPP path uses the existing keyframes as a reference to path generation. Various experiments were conducted to evaluate the KVPP in the real environment and were compared with the conventional gradient path to verify its effectiveness.",
keywords = "Gradient method, Keyframes, Path planner",
author = "Jang, {Min Gyung} and Chae, {Hee Won} and Song, {Jae Bok}",
note = "Funding Information: This work was supported by IITP grant funded by the Korea Government MSIT. (o. 20N 18-0-00622); 19th International Conference on Control, Automation and Systems, ICCAS 2019 ; Conference date: 15-10-2019 Through 18-10-2019",
year = "2019",
month = oct,
doi = "10.23919/ICCAS47443.2019.8971507",
language = "English",
series = "International Conference on Control, Automation and Systems",
publisher = "IEEE Computer Society",
pages = "1050--1053",
booktitle = "ICCAS 2019 - 2019 19th International Conference on Control, Automation and Systems, Proceedings",
}