Abstract
The Simultaneous Localization and Mapping (SLAM) problem is of great significance within the modern field of unmanned systems. However, many current methodologies have high cost implications, utilising expensive Light Detection and Ranging (LIDAR) or Charged Coupled Devices (CCD) sensors to obtain information pertaining to the local topology of the device. The objective of this paper is to reduce the inherent cost of SLAM by generating a motion model which is suitable for use with the low cost Microsoft Kinect sensor system.
A novel filtering methodology is developed which can separate the static and dynamic accelerations in order to compute a full 6 DOF pose estimate from a 3 axis–accelerometer, suitable for application as a SLAM motion model. The filter is seen to operate in constant time, at a frequency sufficient for on–line implementation to a suitable level of accuracy for use with SLAM.
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References
Kamarulzaman, K., Syed, M., Ali, S., Ammar, Z.: Performance Analysis of the Microsoft Kinect Sensor for 2D Simultaneous Localization and Mapping (SLAM) Techniques. Sensors 14, 23365–23387 (2014). ISSN 1424–8220
Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. The MIT Press, Cambridge (2006)
Fahimi, F.: Autonomous Robots: Modelling, Path Planning and Control. Springer, New York (2009)
Singh, K.: Engineering Mathematics Through Applications. Palgrave Macmillan, London (2003)
Wang, J., Garratt, M., Lambert, A., Wang, J.: Integration of GPS/INS/vision sensors to navigate unmanned aerial vehicles. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, ISPRS, vol. 37, part. B1, pp. 963–369 (2008)
Sasiadek, J., Wang, Q., Zeremba, M.: Fuzzy adaptive kalman filtering for INS/GPS data fusion. In: IEEE International Symposium on Intelligent, Control, pp. 181–189 (2000)
Yin-Tien, W., Chin-An, S., Jr-Syu, Y.: Calibrated kinect sensors for robot simultaneous localization and mapping. In: Proceedings of the 8th International Conference on Sensing Technology, pp. 104–109, Liverpool, UK, September 2-4, 2014
Sturm, J., Engelhard, N., Endres, F., Burgard, W., Cremers, D.: A Benchmark for the Evaluation of RGB-D SLAM Systems. In: Proc. of the International Conference on Intelligent Robot Systems (IROS) (2012)
Pedley, M.: Tilt Sensing Using a Three-Axis Accelerometer. Recent Advances in Computer Science and Information Engineering, Free-scale Semiconductor, no. AN3461, version 6, (2013). http://cache.freescale.com/files/sensors/doc/app_note/AN3461.pdf?fpsp=1
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Harman, A., Duran, O., Zweiri, Y. (2015). Novel Model for SLAM in UAS. In: Liu, H., Kubota, N., Zhu, X., Dillmann, R. (eds) Intelligent Robotics and Applications. Lecture Notes in Computer Science(), vol 9246. Springer, Cham. https://doi.org/10.1007/978-3-319-22873-0_7
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DOI: https://doi.org/10.1007/978-3-319-22873-0_7
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-22872-3
Online ISBN: 978-3-319-22873-0
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