Vision-based place recognition is a desirable feature for an autonomous mobile system. In order to work in realistic scenarios, visual recognition algorithms should be adaptive, i.e. should be able to learn from experience and adapt continuously to changes in the environment. This paper presents a discriminative incremental learning approach to place recognition. We use a recently introduced version of the incremental SVM, which allows to control the memory requirements as the system updates its internal representation. At the same time, it preserves the recognition performance of the batch algorithm. In order to assess the method, we acquired a database capturing the intrinsic variability of places over time. Extensive experiments show the power and the potential of the approach. ©2007 IEEE
Incremental learning for place recognition in dynamic environments / Luo, J.; Pronobis, A.; Caputo, Barbara; Jensfelt, P.. - STAMPA. - (2007), pp. 721-728. (Intervento presentato al convegno 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2007 tenutosi a San Diego, CA; USA nel 29 October - 02 November 2007) [10.1109/IROS.2007.4398986].
Incremental learning for place recognition in dynamic environments
CAPUTO, BARBARA;
2007
Abstract
Vision-based place recognition is a desirable feature for an autonomous mobile system. In order to work in realistic scenarios, visual recognition algorithms should be adaptive, i.e. should be able to learn from experience and adapt continuously to changes in the environment. This paper presents a discriminative incremental learning approach to place recognition. We use a recently introduced version of the incremental SVM, which allows to control the memory requirements as the system updates its internal representation. At the same time, it preserves the recognition performance of the batch algorithm. In order to assess the method, we acquired a database capturing the intrinsic variability of places over time. Extensive experiments show the power and the potential of the approach. ©2007 IEEEI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
