TY - GEN
T1 - An image-to-image loop-closure detection method based on unsupervised landmark extraction
AU - Sariyanidi, Evangelos
AU - Şencan, Onur
AU - Temeltaş, Hakan
PY - 2012
Y1 - 2012
N2 - This paper presents a dedicated approach to detect loop closures using visually salient patches. We introduce a novel, energy maximization based saliency detection technique which has been used for unsupervised landmark extraction. We explain how to learn the extracted landmarks on-the-fly and re-identify them. Furthermore, we describe the sparse location representation we use to recognize previously seen locations in order to perform reliable loop closure detection. The performance of our method has been analyzed both on an indoor and an outdoor dataset, and it has been shown that our approach achieves quite promising results on both datasets.
AB - This paper presents a dedicated approach to detect loop closures using visually salient patches. We introduce a novel, energy maximization based saliency detection technique which has been used for unsupervised landmark extraction. We explain how to learn the extracted landmarks on-the-fly and re-identify them. Furthermore, we describe the sparse location representation we use to recognize previously seen locations in order to perform reliable loop closure detection. The performance of our method has been analyzed both on an indoor and an outdoor dataset, and it has been shown that our approach achieves quite promising results on both datasets.
UR - https://www.scopus.com/pages/publications/84864965859
U2 - 10.1109/IVS.2012.6232174
DO - 10.1109/IVS.2012.6232174
M3 - Conference contribution
AN - SCOPUS:84864965859
SN - 9781467321198
T3 - IEEE Intelligent Vehicles Symposium, Proceedings
SP - 420
EP - 425
BT - 2012 IEEE Intelligent Vehicles Symposium, IV 2012
T2 - 2012 IEEE Intelligent Vehicles Symposium, IV 2012
Y2 - 3 June 2012 through 7 June 2012
ER -