Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings |
| Subtitle of host publication | IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2014 |
| Publisher | IEEE Institute of Electrical and Electronic Engineers |
| 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