#263:
H. Aliakbarpour,
J. F. Ferreira,
V. B. S. Prasath,
K. Palaniappan,
G. Seetharaman, and
J. Dias
IEEE Sensors Journal,
Volume 17,
pgs. 2640-2641,
2017
Abstract,
Bibtex,
PlainText,
DOI,
Google Scholar
This letter proposes a framework to perform 3D reconstruction using a heterogeneous sensor network, with potential use in augmented reality (AR), human behavior understanding, smart-room implementations, robotics, and many other applications. We fuse orientation measurements from inertial sensors, images from cameras and depth data from Time of Flight (ToF) sensors within a probabilistic framework in a synergistic manner to obtain robust reconstructions. A fully probabilistic method is proposed to efficiently fuse the multi-modal data of the system.
@article{2017a,
author = "H. Aliakbarpour and J. F. Ferreira and V. B. S. Prasath and K. Palaniappan and G. Seetharaman and J. Dias",
title = "A probabilistic framework for 3D reconstruction using heterogeneous sensors",
year = 2017,
journal = "IEEE Sensors Journal",
volume = 17,
number = 9,
pages = "2640-2641",
month = "May",
keywords = "3d reconstruction, depth sensor, sensor fusion",
doi = "10.1109/JSEN.2017.2679187"
}
H. Aliakbarpour, J. F. Ferreira, V. B. S. Prasath, K. Palaniappan, G. Seetharaman, and J. Dias. A probabilistic framework for 3D reconstruction using heterogeneous sensors. IEEE Sensors Journal, volume 17, issue 9, pages 2640-2641, May 2017.