Real Time Localization and 3D Reconstruction
Abstract
In this paper we describe a method that estimates the motion of a calibrated camera (settled on an experimental vehicle) and the tridimensional geometry of the environment. The only data used is a video input. In fact, interest points are tracked and matched between frames at video rate. Robust estimates of the camera motion are computed in real-time, key-frames are selected and permit the features 3D reconstruction. The algorithm is particularly appropriate to the reconstruction of long images sequences thanks to the introduction of a fast and local bundle adjustment method that ensures both good accuracy and consistency of the estimated camera poses along the sequence. It also largely reduces computational complexity compared to a global bundle adjustment. Experiments on real data were carried out to evaluate speed and robustness of the method for a sequence of about one kilometer long. Results are also compared to the ground truth measured with a differential GPS.
Cite
Text
Mouragnon et al. "Real Time Localization and 3D Reconstruction." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2006. doi:10.1109/CVPR.2006.236Markdown
[Mouragnon et al. "Real Time Localization and 3D Reconstruction." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2006.](https://mlanthology.org/cvpr/2006/mouragnon2006cvpr-real/) doi:10.1109/CVPR.2006.236BibTeX
@inproceedings{mouragnon2006cvpr-real,
title = {{Real Time Localization and 3D Reconstruction}},
author = {Mouragnon, E. and Lhuillier, Maxime and Dhome, Michel and Dekeyser, Fabien and Sayd, Patrick},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2006},
pages = {363-370},
doi = {10.1109/CVPR.2006.236},
url = {https://mlanthology.org/cvpr/2006/mouragnon2006cvpr-real/}
}