Abstract
| Original language | English |
|---|---|
| Title of host publication | Proceedings |
| Subtitle of host publication | IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2014 |
| Publisher | Institute of Electrical and Electronic Engineers IEEE |
| Pages | 6543-6547 |
| ISBN (Electronic) | 978-1-4799-2893-4 |
| ISBN (Print) | 978-147992892-7 |
| DOIs | |
| Publication status | Published - 2014 |
| MoE publication type | A4 Article in a conference publication |
| Event | IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 - Florence, Italy Duration: 4 May 2014 → 9 May 2014 |
Conference
| Conference | IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 |
|---|---|
| Abbreviated title | ICASSP 2014 |
| Country | Italy |
| City | Florence |
| Period | 4/05/14 → 9/05/14 |
Keywords
- algorithms
- localisation
- mapping
- data association
Cite this
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Multiple hypotheses data association propagation for robust monocular-based SLAM algorithms. / Soto-Alvarez, M.; Honkamaa, Petri.
Proceedings: IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2014. Institute of Electrical and Electronic Engineers IEEE, 2014. p. 6543-6547.Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review
TY - GEN
T1 - Multiple hypotheses data association propagation for robust monocular-based SLAM algorithms
AU - Soto-Alvarez, M.
AU - Honkamaa, Petri
PY - 2014
Y1 - 2014
N2 - Data Association is probably the most important step of every monocular Simultaneous Localization and Mapping (SLAM) algorithm because it provides the basic information to the estimation module, independently on the estimation algorithm of choice. Although important, it is also a difficult task because the analytic solution is NP-Hard. The usual approximation is obtaining only one data association hypothesis per frame which affects the robustness of the result [1][2][3][4][5]. In this paper, a data association approach is presented, where multiple hypotheses are propagated between frames using a probabilistic framework. Experimental results, using real and synthetic data, show that the proposed algorithm produces promising results with respect to other state of the art methods.
AB - Data Association is probably the most important step of every monocular Simultaneous Localization and Mapping (SLAM) algorithm because it provides the basic information to the estimation module, independently on the estimation algorithm of choice. Although important, it is also a difficult task because the analytic solution is NP-Hard. The usual approximation is obtaining only one data association hypothesis per frame which affects the robustness of the result [1][2][3][4][5]. In this paper, a data association approach is presented, where multiple hypotheses are propagated between frames using a probabilistic framework. Experimental results, using real and synthetic data, show that the proposed algorithm produces promising results with respect to other state of the art methods.
KW - algorithms
KW - localisation
KW - mapping
KW - data association
U2 - 10.1109/ICASSP.2014.6854865
DO - 10.1109/ICASSP.2014.6854865
M3 - Conference article in proceedings
SN - 978-147992892-7
SP - 6543
EP - 6547
BT - Proceedings
PB - Institute of Electrical and Electronic Engineers IEEE
ER -