Abstract
Precise estimation of the position of robots, which is essential in mobile robotics, is difficult to achieve. However, particle filter shows great promise in this area. The number of samples used in this study is closely related to the operation time in particle filtering. The main issue in real-time implementation with regard to particle filter is to reduce the operation time, which led to the development of the adaptive particle filter (APF). We propose a new APF which adjusts the variance and then uses the gradient data to generate samples near the high likelihood region. The experiment results show that the new APF performs better, in terms of the total operation time and sample set size, than the standard particle filter and the APF using Kullback-Leibler distance sampling.
Original language | English |
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Pages (from-to) | 801-807 |
Number of pages | 7 |
Journal | International Journal of Control, Automation and Systems |
Volume | 8 |
Issue number | 4 |
DOIs | |
Publication status | Published - 2010 Aug |
Keywords
- Kullback-leibler distance
- mobile robot
- particle filter
- ultrasonic beacon
ASJC Scopus subject areas
- Control and Systems Engineering
- Computer Science Applications