Author:
Jie Luo,
Department of Computer and Systems Sciences,
Stockholm University / Royal Institute of Technology
Summary:
Vision-based place recognition is a desirable feature for an autonomous mobile system. In order to work in realistic scenarios, a visual recognition algorithm should have two key properties: robustness and adaptability. This thesis focuses on the latter, and presents a discriminative incremental learning approach to place recognition. We propose a solution based on incremental extensions of support vector machine classifier. Since computational and memory efficiency are crucial for mobile robot platforms aim to continoulsy work in real-world settings, we put emphasis on these properties. We use a recently introduced memory-controlled incremental technqiue, 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 and runs online. 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.
Moreover, the feature space of SVM consists of both global feature - Composed Receptive Field Histograms, and local features - Harris-Laplace Detector and SIFT Descriptor.
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