Abstract
The issue of simultaneous localization and mapping (SLAM) is an excellent technology. Normally, the current measurements need to be compared with all existing landmarks. However, the accuracy of the estimated location of the robot will decrease because of incorrect data association. To solve these problems, this paper presents a novel architecture for SLAM. The fuzzy filter and curvature data are used to filter current measurement to retain special measurements and avoid wrong landmarks. In addition, triangulation is used to improve the accuracy of the robot's location. The effectiveness of the proposed algorithm is showed by means of simulation results.
| Original language | English |
|---|---|
| Title of host publication | CACS 2014 - 2014 International Automatic Control Conference, Conference Digest |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 113-116 |
| Number of pages | 4 |
| ISBN (Electronic) | 9781479945849 |
| DOIs | |
| Publication status | Published - 2014 Apr 28 |
| Event | 2014 International Automatic Control Conference, CACS 2014 - Kaohsiung, Taiwan Duration: 2014 Nov 26 → 2014 Nov 28 |
Other
| Other | 2014 International Automatic Control Conference, CACS 2014 |
|---|---|
| Country | Taiwan |
| City | Kaohsiung |
| Period | 14/11/26 → 14/11/28 |
Keywords
- FastSLAM
- K-value curvatur
- extended Kalman filter
- fuzzy filter
- particle filter
ASJC Scopus subject areas
- Control and Systems Engineering
Fingerprint Dive into the research topics of 'Improved SLAM algorithm using fuzzy filter and curvature data association'. Together they form a unique fingerprint.
Cite this
Shih, Y. J., Hsu, C-C. J., Wang, W-Y., & Wang, Y. T. (2014). Improved SLAM algorithm using fuzzy filter and curvature data association. In CACS 2014 - 2014 International Automatic Control Conference, Conference Digest (pp. 113-116). [7097172] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CACS.2014.7097172