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Self-localization is one fundamental problem in robotics, and important for various tasks. Most previous methods for self-localization is based on comparison between an environment-map and observed features (or landmarks). These approaches often fail in a dynamic and large environment with noisy sensors. To solve this problem, we propose a vision-based(More)
In recent years, high-dimensional descriptive features have been widely used for feature-based robot localization. However, the space/time costs of building/retrieving the map database tend to be significant due to the high dimensionality. In addition, most of existing databases are working well only on batch problems, difficult to be built incrementally by(More)
We propose a vision-based method for detecting and tracking a mobile robot in dynamic, complex and unstructured environments, such as an office. When there are many moving objects (e.g. robot and persons) in the environment, and they interact with each other, it is not easy to estimate the correct correspondence between detected moving objects and(More)
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