Abstract
AbstractIn this work, we face the problem of estimating the relative position and orientation of a camera and an object, when they are both equipped with inertial measurement units (IMUs), and the object exhibits a set of n landmark points with known coordinates (the so-called Pose estimation or PnP Problem). We present two algorithms that, fusing the information provided by the camera and the IMUs, solve the PnP problem with good accuracy. These algorithms only use the measurements given by IMUs’ inclinometers, as the magnetometers usually give inaccurate estimates of the Earth magnetic vector. The effectiveness of the proposed methods is assessed by numerical simulations and experimental tests. The results of the tests are compared with the most recent methods proposed in the literature.
Funder
Università della Calabria
Publisher
Springer Science and Business Media LLC
Subject
Computer Science Applications,Computer Vision and Pattern Recognition,Hardware and Architecture,Software
Reference39 articles.
1. Fischler, M.A., Bolles, R.C.: Random sample consensus: a para-digm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24, 381–395 (1981)
2. Hartley, R., Zisserman, A.: Multiple view geometry in computer vision, 2nd edn. Cambridge University Press, Cambridge (2003)
3. Forsyth, D., Ponce, J.: Computer vision: a modern approach, 2nd edn. Prentice Hall, London (2012)
4. Marchand, E., Uchiyama, H., Spindler, F.: Pose estimation for augmented reality: a hands-on survey. IEEE Trans. Vis. Comput. Graph 22(12), 2633–2651 (2016)
5. McGlone, J., Mikhail, E., Bethel, J., Manual of photogrammetry, 5th Ed., American Society for Photogrammetry and Remote Sensing (2004)
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