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Yang, P (2012) Efficient particle filter algorithm for ultrasonic sensor-based 2D range-only simultaneous localisation and mapping application. IET Wireless Sensor Systems, 2 (4). pp. 394-401. ISSN 2043-6386
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Ultrasonic Sensor for IET.pdf - Accepted Version Download (682kB) | Preview |
Abstract
Owing to low cost and relatively accurate range measurement, ultrasonic sensors are widely used in various simultaneous localisation and mapping (SLAM) applications. In spite of the abundance of ultrasonic sensor based SLAM applications, a simple and efficient algorithm for an ultrasonic sensor based positioning system with good accuracy and low computational complexity has not yet emerged. The major difficulty is the trade-off between localisation accuracy and computational complexity in most SLAM algorithms, such as extended Kalman filter (EKF) and particle filter. Typically, they improve localisation accuracy by increasing the density of the landmarks, as a result leading to high computational complexity of algorithms and limiting the utilisation of algorithms into online SLAM systems. This study addresses an improved particle filter algorithm to solve ultrasonic sensor based 2D range-only SLAM problem with relatively good accuracy and low computational complexity. This algorithm uses a simple four fixed features based system model to limit the density of the landmarks. A technique called map adjustment is proposed to increase the accuracy and efficiency of the algorithm. Using map adjustment, the proposed particle filter algorithm can improve localisation accuracy and lower computational complexity. The experiment results demonstrate that this algorithm has a better performance than conventional particle filter localisation algorithm.
| Item Type: | Article |
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| Additional Information: | This paper is a postprint of a paper submitted to and accepted for publication in IET Wireless Sensor Systems and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at IET Digital Library |
| Subjects: | Q Science > QA Mathematics |
| Divisions: | Computer Science & Mathematics |
| Publisher: | IET |
| Date Deposited: | 30 Sep 2015 14:03 |
| Last Modified: | 04 Sep 2021 13:56 |
| DOI or ID number: | 10.1049/iet-wss.2011.0129 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/2103 |
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