Unsupervised Place Discovery for Place-Specific Change Classifier

7 Jun 2017Fei XiaoxiaoTanaka Kanji

In this study, we address the problem of supervised change detection for robotic map learning applications, in which the aim is to train a place-specific change classifier (e.g., support vector machine (SVM)) to predict changes from a robot's view image. An open question is the manner in which to partition a robot's workspace into places (e.g., SVMs) to maximize the overall performance of change classifiers... (read more)

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