Online and Incremental Appearance-based SLAM in Highly Dynamic Environments

This paper presents a novel method for online and incremental appearance-based localization and mapping in a highly dynamic environment. Using position-invariant robust features (PIRFs), the method can achieve a high rate of recall with 100% precision. It can handle both strong perceptual aliasing and dynamic changes of places efficiently. Its performance also extends beyond conventional images; it is applicable to omnidirectional images for which the major portions of scenes are similar for most places. The proposed PIRF-based Navigation method named PIRF-Nav is evaluated by testing it on two standard datasets as is in FAB-MAP and on an additional omnidirectional image dataset that we collected. This extra dataset is collected on two days with different specific events, i.e., an open-campus event, to present challenges related to illumination variance and strong dynamic changes, and to test assessment of dynamic scene changes. Results show that PIRF-Nav outperforms FAB-MAP; PIRF-Nav ...

• Type: Journal
• Year: 2011
• Where: IJRR
• Authors: Aram Kawewong, Noppharit Tongprasit, Sirinart Tangruamsub, Osamu Hasegawa
• Keywords: Dynamic Changes, IJRR 2011, Incremental Appearance-based Localization, Omnidirectional Image