Pregled bibliografske jedinice broj: 1023063
NOSeqSLAM: Not only Sequential SLAM
NOSeqSLAM: Not only Sequential SLAM // Iberian Robotics Conference
Porto, Portugal, 2019. str. 1-12 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 1023063 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
NOSeqSLAM: Not only Sequential SLAM
Autori
Maltar, Jurica ; Marković, Ivan ; Petrović, Ivan
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Iberian Robotics Conference
/ - , 2019, 1-12
Skup
4th Iberian Robotics Conference (ROBOT 2019)
Mjesto i datum
Porto, Portugal, 20.11.2019. - 22.11.2019
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
Visual place recognition ; Localization ; SeqSLAM ; deep convolutional neural networks
Sažetak
The essential property that every autonomous system should have is the ability to localize itself, i.e., to reason about its location relative to measured landmarks and leverage this information to consistently estimate vehicle location through time. One approach to solving the localization problem is visual place recognition. Using only camera images, this approach has the following goal: during the second traversal through the environment, using only current images, find the match in the database that was created during a previously driven traversal of the same route. Besides the image representation method – in this paper we use feature maps extracted from the OverFeat architecture – for visual place recognition it is also paramount to perform the scene matching in a proper way. For autonomous vehicles and robots traversing through an environment, images are acquired sequentially, thus visual place recognition localization approaches use the structure of sequentiality to locally match image sequences to the database for higher accuracy. In this paper we propose a not only sequential approach to localization ; specifically, in- stead of linearly searching for sequences, we construct a directed acyclic graph and search for any kind of sequences. We evaluated the proposed approach on a dataset consisting of varying environmental conditions and demonstrated that it outperforms the SeqSLAM approach.
Izvorni jezik
Engleski
Znanstvena područja
Elektrotehnika, Računarstvo, Temeljne tehničke znanosti
POVEZANOST RADA
Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb,
Sveučilište u Osijeku, Odjel za matematiku