Autonomous Object Discovery on a Mobile Platform
نویسندگان
چکیده
This paper studies the problem of a mobile agent autonomously discovering objects within an unknown environment. We demonstrate an unsupervised clustering algorithm capable of grouping environmental features into distinct groups, and show how this clustering algorithm can be used to segment the world into seperate objects. The problem of learning the structure of the environment is also addressed, and a spring-inspired model for solving the simultaneous localization and mapping (SLAM) problem is used to map the environment.
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