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Integrated Inertial Navigation System with camera-based system using LabVIEW Software

M. Raja1 , Anmol Agarwal2 , Anushree Gautam3

Section:Research Paper, Product Type: Journal-Paper
Vol.8 , Issue.1 , pp.59-64, Feb-2020


CrossRef-DOI:   https://doi.org/10.26438/ijsrpas/v8i1.5964


Online published on Feb 28, 2020


Copyright © M. Raja, Anmol Agarwal, Anushree Gautam . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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IEEE Style Citation: M. Raja, Anmol Agarwal, Anushree Gautam, “Integrated Inertial Navigation System with camera-based system using LabVIEW Software,” International Journal of Scientific Research in Physics and Applied Sciences, Vol.8, Issue.1, pp.59-64, 2020.

MLA Style Citation: M. Raja, Anmol Agarwal, Anushree Gautam "Integrated Inertial Navigation System with camera-based system using LabVIEW Software." International Journal of Scientific Research in Physics and Applied Sciences 8.1 (2020): 59-64.

APA Style Citation: M. Raja, Anmol Agarwal, Anushree Gautam, (2020). Integrated Inertial Navigation System with camera-based system using LabVIEW Software. International Journal of Scientific Research in Physics and Applied Sciences, 8(1), 59-64.

BibTex Style Citation:
@article{Raja_2020,
author = {M. Raja, Anmol Agarwal, Anushree Gautam},
title = {Integrated Inertial Navigation System with camera-based system using LabVIEW Software},
journal = {International Journal of Scientific Research in Physics and Applied Sciences},
issue_date = {2 2020},
volume = {8},
Issue = {1},
month = {2},
year = {2020},
issn = {2347-2693},
pages = {59-64},
url = {https://www.isroset.org/journal/IJSRPAS/full_paper_view.php?paper_id=1710},
doi = {https://doi.org/10.26438/ijcse/v8i1.5964}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v8i1.5964}
UR - https://www.isroset.org/journal/IJSRPAS/full_paper_view.php?paper_id=1710
TI - Integrated Inertial Navigation System with camera-based system using LabVIEW Software
T2 - International Journal of Scientific Research in Physics and Applied Sciences
AU - M. Raja, Anmol Agarwal, Anushree Gautam
PY - 2020
DA - 2020/02/28
PB - IJCSE, Indore, INDIA
SP - 59-64
IS - 1
VL - 8
SN - 2347-2693
ER -

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Abstract :
This research exhibits an integrated Inertial Navigation System (INS) and a camera-based navigation system. The system contains three types of sensors such as A camera, capturing the terrain. The raw image data are fed into an image-processing algorithm, the output of which is a set of recognizable points in the image, An IMU (Inertial Measurement Unit) containing gyroscopes and accelerometers, magnetometer. The miniaturization, reduced cost, and increased accuracy of cameras and inertial measurement units (IMU) makes them ideal sensors for determining the 3D position and attitude of vehicles navigating in GPS-denied areas. In particular, fast and highly dynamic motions can be precisely estimated over short periods of time by fusing rotational velocity and linear acceleration measurements provided by the IMU`s gyroscopes and accelerometers, respectively. The integration of the bias and noise in the inertial measurements can be significantly reduced by processing observations to point features detected in camera images in what is known as a vision-aided inertial navigation system.

Key-Words / Index Term :
INS, IMU, 3D Position, Gyroscope, Accelerometer

References :
[1] A. I. Mourikis and S. I. Roumeliotis, “A multi-state constraint Kalman filter for vision-aided inertial navigation,” in Proc. of the IEEE Intl. Conf. on Robot. and Automous., Roma, Italy, Apr. 2007, pp. 3565–3572.
[2] D. G. Kottas, J. A. Hesch, S. L. Bowman, and S. I. Roumeliotis, “On the consistency of vision-aided inertial navigation,” in Proc. of the Intl. Sym. on Exp. Robot., Quebec, Canada, June 2012.
[3] L. Meier, P. Tanskanen, F. Fraundorfer, and M. Pollefeys, “Pixhawk: A system for autonomous flight using onboard computer vision,” in Proc. of the IEEE Intl. Conf. on Robot. and Autom., Shanghai, China, May 2011, pp. 2992–2997.
[4] E. S. Jones and S. Soatto, “Visual-inertial navigation, mapping and localization: A scalable real-time causal approach,” Intl. J. Robot. Research, vol. 30, no. 4, pp. 407–430, Apr. 2011.
[5] Bertozzi M., Broggi A., GOLD: A parallel real-time stereo vision system for generic obstacle and lane detection. IEEE Trans. Image Process. 1998;7:62-81.
[6] Murray D., Little J.J. Using real-time stereo vision for mobile robot navigation. Autonomous. Rob. 2000;8:161-171.

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