ABSTRACT
This paper deals with navigation indoors and in small-scale outdoor scenes. We propose to enhance IMU and conventional visual odometry navigation - typically used in the said contexts - by visual location estimation based on markers conveniently displaced in the environment. We propose using visually appealing Uniform Marker Fields; this technique offers high precision and real-time performance on mobile devices of moderate computational power. We evaluate the accuracy of the proposed technique, achieving approximately 4cm error in a scene of roughly 5 m diameter (beneath 1 %).
- Assa, A., and Janabi-Sharifi, F. 2015. A kalman filter-based framework for enhanced sensor fusion. Sensors Journal, IEEE.Google Scholar
- Burns, J., and Mitchell, C. J. 1993. Coding schemes for two-dimensional position sensing. Institute of Mathematics and Its Applications Conference Series 45, 31.Google Scholar
- Chiu, H.-P., Williams, S., Dellaert, F., Samarasekera, S., and Kumar, R. 2013. Robust vision-aided navigation using sliding-window factor graphs. In Robotics and Automation (ICRA), 2013 IEEE International Conference on.Google Scholar
- Fiala, M. 2005. ARTag, a fiducial marker system using digital techniques. In Proc. CVPR. Google ScholarDigital Library
- Fiala, M. 2010. Designing highly reliable fiducial markers. IEEE PAMI 32, 7 (July), 1317--1324. Google ScholarDigital Library
- Herout, A., Szentandrási, I., Zachariáš, M., Dubská, M., and Kajan, R. 2013. Five shades of grey for fast and reliable camera pose estimation. In Proceedings of CVPR, IEEE Computer Society, 1384--1390. Google ScholarDigital Library
- Hirzer, M. 2008. Marker detection for augmented reality applications. Tech. rep., Inst. for Comp. Graphics and Vision, Graz Univ. of Tech., AT.Google Scholar
- Kato, H., and Billinghurst, M. 1999. Marker tracking and HMD calibration for a video-based ar conferencing system. In Proc. IWAR. Google ScholarDigital Library
- Klein, G., and Murray, D. 2007. Parallel tracking and mapping for small AR workspaces. In ISMAR. Google ScholarDigital Library
- Leutenegger, S., Melzer, A., Alexis, K., and Siegwart, R. 2014. Robust state estimation for small unmanned airplanes. In Control Applications (CCA), 2014 IEEE Conference on, 1003--1010.Google Scholar
- Oskiper, T., Samarasekera, S., and Kumar, R. 2012. Multi-sensor navigation algorithm using monocular camera, IMU and GPS for large scale augmented reality. In 11th IEEE International Symposium on Mixed and Augmented Reality, IS-MAR 2012, Atlanta, GA, USA, November 5-8, 2012, IEEE Computer Society. Google ScholarDigital Library
- Paces, P., and Suchy, J. 2014. Statistical evaluation of multiple low-cost mems sensors for altitude measurement. In Digital Avionics Systems Conference (DASC), 2014 IEEE/AIAA 33rd.Google Scholar
- Schaffalitzky, F., and Zisserman, A. 2000. Planar grouping for automatic detection of vanishing lines and points. Image and Vision Computing 18, 647--658.Google ScholarCross Ref
- Szentandrási, I., Zachariáš, M., Havel, J., Herout, A., Dubská, M., and Kajan, R. 2012. Uniform Marker Fields: Camera loc. by orientable De Bruijn tori. In ISMAR.Google Scholar
- Wald, A. 1945. Sequential tests of statistical hypotheses. The Annals of Mathematical Statistics 16, 2, 117--186.Google ScholarCross Ref
- Zhang, Z. 2000. A flexible new technique for camera calibration. Pattern Analysis and Machine Intelligence, IEEE Transactions on 22, 11 (Nov), 1330--1334. Google ScholarDigital Library
- Zhao, H., and Wang, Z. 2012. Motion measurement using inertial sensors, ultrasonic sensors, and magnetometers with extended kalman filter for data fusion. Sensors Journal, IEEE.Google Scholar
- Zhu, Z., Oskiper, T., Samarasekera, S., Kumar, R., and Sawhney, H. 2007. Ten-fold improvement in visual odometry using landmark matching. In Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on.Google Scholar
Index Terms
- Visual correction of position drift using uniform marker fields
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