Analysis of robot localisation performance based on extended kalman filter
This thesis presents the Simultaneous Localization and Mapping (SLAM) problem for a mobile robot in an unknown indoor environment. A most current localisation algorithm has less flexibility and autonomy because it depends on human to determine what aspects of the sensor data to use in localisation....
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| Format: | Thesis |
| Language: | English English English |
| Published: |
2015
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| Online Access: | http://eprints.uthm.edu.my/1313/2/FARAH%20HAZWANI%20CHE%20HASSAN%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/1313/1/24p%20FARAH%20HAZWANI%20CHE%20HASSAN.pdf http://eprints.uthm.edu.my/1313/3/FARAH%20HAZWANI%20CHE%20HASSAN%20WATERMARK.pdf |
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| Summary: | This thesis presents the Simultaneous Localization and Mapping (SLAM) problem for a mobile robot in an unknown indoor environment. A most current localisation algorithm has less flexibility and autonomy because it depends on human to determine what aspects of the sensor data to use in localisation. To improve the localisation accuracy for a mobile robot, the Extended Kalman Filter (EKF) algorithm is used to achieve the required robustness and accuracy. EKF is a technique from estimation theory that combines the information of different uncertain sources to obtain the value of variables. However, there are a number of variations of EKF with different values of variables, which lead to contradicting results in terms of standard deviations of path (distance) and angle. This project is implemented based on the existing localisation algorithm [30]. There are two types of results that have been analysed in this paper. First is the performance of the algorithm using different parameters in which different velocities and number of landmarks have been used to determine the accuracy of the localisation method. Second is comparing the performance of update approaches of filters namely Kalman Filter Joseph, Kalman Filter Cholesky and Kalman Filter Update in different scenarios. MATLAB coding [30] is used to run the simulation of update approaches of filters. Finding the best variation and a good choice of variables are important factors to have acceptable results consistently. |
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