Environment-Specific Novelty Detection
نویسندگان
چکیده
Novelty detection, recognising features that differ from those that are normally seen, is a potentially useful ability for a mobile robot. Once a robot can detect those features that are novel the amount of learning that has to be done can be reduced (as only new things need to be learnt), the attention of the robot can be focused onto the new features, and the robot can be used as an inspection agent. However, features that are novel in one place could be completely normal elsewhere – for example, tables and chairs are usually seen in offices, but very rarely seen in corridors. This paper suggests a method by which a set of novelty filters can be trained for different environments and the correct filter autonomously selected for the environment that the robot is currently travelling in. The method can also extend itself, so that further environments that are seen by the robot can be added without any retraining.
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