MATEC Web of Conferences
Volume 42, 20162015 The 3rd International Conference on Control, Mechatronics and Automation (ICCMA 2015)
|Number of page(s)||6|
|Section||Robot design and development|
|Published online||17 February 2016|
Multi-modal mobile sensor data fusion for autonomous robot mapping problem
Robotics and Autonomous Systems (RAS) Research Group, German University in Cairo (GUC), 5th settlement - New Cairo, 11432, Cairo, Egypt
Perception is the first step for a mobile robot to perform any task and for it to gain perception mobile robots use sensors to measure the states which represent the surrounding environment. Sensors measurements are always combined with some sort of uncertainty and noise. Which can make the system very unstable and unreliable. In order to get better readings we can always use better types of sensors where we come to a trade off between price and quality. And that’s why our proposed approach to solve this problem was to use data fusion techniques to eliminate the noise and reduce the uncertainty in the readings. The topic of data fusion has been under extensive research in the past decade many approaches had been suggested and yet the research on data fusion is increasing and this because of its importance and applications. This study discuss the use of probabilistic data fusion techniques to reduce the uncertainty and eliminate the noise of the measurements from range finder active sensors to improve the task of mapping for mobile robots. The data fusion methods used were Kalman filter and Bayes filter.
Key words: Mobile Robots / Data fusion / sensor noise / uncertainty / Kalman filter / Bayes filter / Ultrasonic
© Owned by the authors, published by EDP Sciences, 2016
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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