In this paper, a novel approach to visual self localization in an unknown environment is presented. The proposed method makes possible the recognition of new landmark without using GPS or any other communication links or pre-training. An image-based self-localization technique is used to automatically label landmarks that are detected in real-time using a computationally efficient and recursive algorithm. Real-time experiments are carried in outdoor environment at Lancaster University using a real mobile robot Pioneer 3DX in order to build a map the local environment without using any communication links. The presented experimental results in real situations show the effectiveness of the proposed method.
A new approach to real-time knowledge extraction from streaming data generated by wearable wireless accelerometers based on self-learning evolving fuzzy rule-based classifier is proposed and evaluated in this paper. Based on experiments with real subjects we collected data from 18 different classifieds activities. After preprocessing and classifying data depending on the sequence of activities regarding time, we achieved up to 99.81% of accuracy in recognizing a sequence of activities. This technique allows re-training the system as long as the application is running on the wearable intelligent/smart sensor, getting a better classification rate throughout the time without an increase of the delay in performance.
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