Incremental learning for place recognition in dynamic environments

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.

• Type: Conference
• Year: 2007
• Where: IROS
• Authors: Jie Luo, Andrzej Pronobis, Barbara Caputo, Patric Jensfelt
• Keywords: IROS 2007, Place Recognition, Robotics, Vision-based Place Recognition, Visual Recognition Algorithms