Online 3D Reconstruction and 6-DoF Pose Estimation for RGB-D Sensors
Abstract
In this paper, we propose an approach to Simultaneous Localization and Mapping (SLAM) for RGB-D sensors. Our system computes 6-DoF pose and sparse feature map of the environment. We propose a novel keyframe selection scheme based on the Fisher information, and new loop closing method that utilizes feature-to-landmark correspondences inspired by image-based localization. As a result, the system effectively mitigates drift that is frequently observed in visual odometry system. Our approach gives lowest relative pose error amongst any other approaches tested on public benchmark dataset. A set of 3D reconstruction results on publicly available RGB-D videos are presented.
Cite
Text
Lim et al. "Online 3D Reconstruction and 6-DoF Pose Estimation for RGB-D Sensors." European Conference on Computer Vision Workshops, 2014. doi:10.1007/978-3-319-16178-5_16Markdown
[Lim et al. "Online 3D Reconstruction and 6-DoF Pose Estimation for RGB-D Sensors." European Conference on Computer Vision Workshops, 2014.](https://mlanthology.org/eccvw/2014/lim2014eccvw-online/) doi:10.1007/978-3-319-16178-5_16BibTeX
@inproceedings{lim2014eccvw-online,
title = {{Online 3D Reconstruction and 6-DoF Pose Estimation for RGB-D Sensors}},
author = {Lim, Hyon and Lim, Jongwoo and Kim, H. Jin},
booktitle = {European Conference on Computer Vision Workshops},
year = {2014},
pages = {238-254},
doi = {10.1007/978-3-319-16178-5_16},
url = {https://mlanthology.org/eccvw/2014/lim2014eccvw-online/}
}